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A recent report from researchers at Birmingham Women's and Children's NHS Foundation Trust discusses the diagnosis and treatment of progressive familial intrahepatic cholestasis (PFIC), a rare liver disorder that primarily affects children. The researchers aimed to provide recommendations for the management of PFIC in clinical practice. They developed an algorithm for the diagnosis and treatment of children with suspected PFIC, which includes the use of licensed inhibitors of ileal bile acid transporters as the first-line treatment. The authors hope that these recommendations will help standardize the management of PFIC and raise awareness of current developments in the field. [Extracted from the article]

A report from Princess Nourah Bint Abdulrahman University in Riyadh, Saudi Arabia discusses the development of an improved Kepler Optimization Algorithm (I-KOA) for feature selection in liver disease classification. Liver diseases are a significant healthcare challenge globally, and accurate diagnosis is complex. The I-KOA algorithm combines Opposition-Based Learning and a Local Escaping Operator to achieve robust feature selection and high classification accuracy. The algorithm outperforms seven rival optimization algorithms, achieving an overall classification accuracy of 93.46% across multiple liver disease datasets. The researchers believe that I-KOA has the potential to revolutionize medical diagnosis and decision support systems. [Extracted from the article]

A research paper from the Kalasalingam Academy of Research and Education in Tamil Nadu, India, introduces a model called Taylor-Tunicate Swarm Algorithm-based Generative Adversarial Networks (Taylor-TSA-based GANs) for cancer prediction. The model incorporates the Taylor series with the Tunicate Swarm Algorithm and uses the Yeo-Johnson transformation for data transformation. The feature fusion is evaluated by Deep Stacked Autoencoder, and the fused feature is given as input to the cancer prediction done by GAN trained by Taylor-TSA. The research concludes that the developed model is effective and efficient, with high accuracy, a low false positive rate, and a high true positive rate. [Extracted from the article]

A new advanced artificial intelligence (AI) algorithm developed by researchers at Penn State College of Medicine may improve predictions and therapies for autoimmune diseases. The algorithm analyzes the genetic code underlying these conditions to accurately model gene expression and regulation, as well as identify additional genes of risk. The algorithm outperforms existing methodologies and identified 26% more novel gene and trait associations. The researchers also used the algorithm to identify potential therapeutics for autoimmune diseases, pointing to drug compounds that could reverse gene expression in specific cell types associated with these diseases. [Extracted from the article]

A report from Yangtze University in Jingzhou, China discusses the use of deep learning algorithms in food safety and authenticity detection. The researchers highlight the advantages of deep learning algorithms over traditional machine learning algorithms and chemometric algorithms. They specifically focus on convolutional neural networks (CNN), fully convolutional networks (FCN), and generative adversarial networks (GAN). The report also explores the potential for further development in this field, including the use of data augmentation and multimodal neural networks. The research concludes that deep learning algorithms have made significant progress in food safety and authenticity detection, but there is still room for improvement. [Extracted from the article]

A report from the China University of Petroleum (East China) discusses the need for modern technology, specifically artificial intelligence, to support the diagnosis of breast cancer. The existing computer-aided diagnostic systems for breast cancer are costly and limited in their capabilities. The report proposes the use of improved algorithms and a simple integrated system for pre-diagnosis of breast cancer based on CNN and Pyqt5. The system utilizes tensorflow and pytorch frameworks and includes a human-computer interaction GUI system interface. The research has been peer-reviewed and more information can be obtained from the China University of Petroleum (East China). [Extracted from the article]

A recent study conducted at Shiraz University aimed to improve the accuracy of breast cancer diagnosis by using a feature selection algorithm based on particle swarm optimization (PSO) combined with machine learning methods. The researchers tested their algorithm on three common breast cancer datasets and found that it significantly improved accuracy while reducing the number of features needed for diagnosis. The study suggests that the proposed algorithm has the potential to enhance breast cancer diagnosis and reduce cost and time. Further information on the study can be found in the International Journal of Computational Intelligence Systems. [Extracted from the article]

A recent report from Lanzhou University in China discusses the use of a new algorithm called Hierarchical Learning-Enhanced Chaotic Crayfish Optimization Algorithm (HLCCOA) to improve the accuracy of extreme learning machine diagnostics in breast cancer. The HLCCOA algorithm addresses the limitations of traditional crayfish optimization algorithms by utilizing chaotic sequences for population position initialization and implementing a hierarchical learning mechanism. Rigorous testing shows that the HLCCOA-optimized extreme learning machine model outperforms other benchmark models in terms of accuracy, sensitivity, and specificity for breast cancer diagnosis. This research highlights the potential practicality and robustness of the HLCCOA algorithm in improving breast cancer diagnostics. [Extracted from the article]

A recent study conducted by researchers at Wenzhou University in China focuses on improving the accuracy of image segmentation for breast cancer diagnosis. The study proposes a refined whale optimization algorithm that combines a quantum phase interference mechanism and an enhanced solution quality strategy. The algorithm is compared to other state-of-the-art algorithms and is tested on benchmark datasets to validate its performance. The results demonstrate the effectiveness of the segmentation method in both low and high threshold levels, making it a valuable technique for practical applications. The study has been peer-reviewed and provides potential avenues for further research in breast cancer diagnosis. [Extracted from the article]

A study conducted by researchers at the Universidad Autonoma de Zacatecas in Mexico focuses on the early detection of type 2 diabetes mellitus (T2DM) using machine learning algorithms. The study aims to identify potential biomarkers for T2DM and determine any differences between male and female patients. The researchers used clinical and anthropometric variables from patients with T2DM and control patients without T2DM. The results showed that biomarkers such as systolic blood pressure and triglycerides were significantly associated with T2DM. The most accurate machine learning model was selection with recursive feature elimination and random forest as the estimator, achieving an accuracy of 0.8820. The findings of this study have the potential to improve early detection and management of T2DM by considering the differences between male and female patients. Further research is needed to validate these biomarkers in other populations and clinical settings. [Extracted from the article]

A recent study conducted by researchers at Princess Nourah bint Abdulrahman University in Riyadh, Saudi Arabia, has developed a new approach called Swin-GA-RF to enhance the classification of cervical cells in Pap smear images for the detection of cervical cancer. The Swin-GA-RF approach combines the Swin transformer, genetic algorithm feature selection, and a random forest classifier. The researchers compared the performance of Swin-GA-RF with pre-trained CNN models and found that Swin-GA-RF outperformed other models in both binary and five-class classification tasks. These findings suggest that the Swin-GA-RF approach could be a valuable tool for early diagnosis and screening programs for cervical cancer. [Extracted from the article]

Researchers at the University of Mustapha Stambouli in Algeria have developed a new method for accurately classifying breast cancer using machine learning techniques. The method, called Correlation technique-Modified Grey Wolf Optimizer (CMGWO), involves two stages: feature selection and classification. The researchers used the Wisconsin Diagnostic Breast Cancer (WDBC) database to test the performance of their method and found that it significantly increased the accuracy rate of classification. The study concludes that CMGWO is a reliable approach for detecting breast cancer, with an accuracy rate of 99.12%. [Extracted from the article]

A recent report from the School of Electronics Engineering discusses research on brain cancer and the challenges of accurately separating tumor regions from healthy tissue in medical imaging. The researchers propose a novel semantic segmentation architecture called IC-Net, which combines elements from various models to improve brain tumor segmentation in MRI images. The IC-Net design outperforms other segmentation techniques, achieving notable outcomes in accuracy, loss, specificity, sensitivity, and Dice similarity coefficient. This research has the potential to enhance the accuracy of brain tumor segmentation and improve medical imaging analysis in the field of oncology. [Extracted from the article]

A recent report from Universitas Multimedia Nusantara highlights the global concern of breast cancer, which is the leading cancer type in terms of new cases and among the top five cancer types with the most deaths. The research aims to improve the identification of breast cancer comorbidities and ultimately reduce the number of deaths from this condition. The study utilizes network analysis and community detection algorithms to identify comorbidities associated with breast cancer. The findings reveal several diseases that are commonly associated with breast cancer, providing valuable insights for medical professionals. [Extracted from the article]

A research study conducted at the KLS Gogte Institute of Technology in Belagavi, India, has explored the use of super-resolution (SR) reconstruction techniques for improving the spatial resolution of pathology images in prostate cancer diagnosis. The study compared various machine learning, deep learning, and generative adversarial network (GAN) algorithms and evaluated their performance using different metrics. The results showed that the SRGAN algorithm outperformed other algorithms, achieving high-quality image resolution and fine details. The application of SRGAN could provide a cost-effective solution for enhancing the accuracy of cancer diagnosis by improving the visualization of cellular and tissue structures in pathology images. [Extracted from the article]

A recent study conducted at Kanazawa University in Japan explores the use of machine learning algorithms for the diagnosis of cervical cancer. The researchers propose a novel feature selection strategy called the Binary Harris Hawks Optimization (BHHO) algorithm, which aims to reduce the number of features used in diagnosis while improving accuracy. The BHHO algorithm demonstrates better stability and accuracy compared to other common wrapper-based feature selection methods, not only on cervical cancer datasets but also on other disease datasets. This research contributes to the ongoing efforts to improve early diagnosis and treatment of cervical cancer, which is one of the leading causes of cancer-related deaths among females worldwide. [Extracted from the article]

A recent study conducted by researchers at the University of Granada in Spain has developed a new app called PaiNEd that aims to assess pain experience in survivors of breast cancer. The app includes an algorithm that determines whether patients are experiencing pain predominantly related to central sensitization (CS) symptoms. The study found that the app demonstrated reliability in evaluating pain experience and could be a valuable tool for healthcare professionals involved in pain education programs. This research provides a potential solution for assessing and managing pain in breast cancer survivors. [Extracted from the article]

A recent report from Universiti Malaya discusses research on cancer treatment using optimal control supported by swarm intelligence. The study aims to develop hybrid techniques that combine optimal control theory with evolutionary algorithms and multi-objective swarm algorithms to reduce cancerous cells while minimizing the use of chemotherapy medications and toxicity. Two hybrid techniques were proposed and evaluated, with MOEA/D emerging as the most effective algorithm in reducing tumor size and cancer drug concentration. The study highlights the importance of integrating optimal control theory and evolutionary algorithms for optimizing cancer chemotherapy treatment. [Extracted from the article]

A recent report from investigators at the School of Computing in Tamil Nadu, India, discusses the use of swarm intelligent metaheuristic optimization algorithms-based artificial neural network models for breast cancer diagnosis. The report emphasizes the importance of early detection and the use of optimization algorithms to improve accuracy and efficiency in predicting breast cancer. The researchers compare different approaches and highlight the significance of swarm intelligent algorithms-based optimized artificial neural network models. The report also discusses evaluation metrics and future research directions in this field. This research has been peer-reviewed and provides valuable insights into breast cancer diagnosis. [Extracted from the article]

A study conducted by researchers at Guangxi University in China has proposed a new model for staging liver fibrosis based on image processing technology. The model, called EVO-MS, utilizes an ensemble learning approach and the energy valley optimization algorithm to select informative features for fibrosis quantification. The researchers found that selecting 3-5 feature parameters yielded satisfactory results in classification, with certain features significantly influencing accuracy. The EVO-MS model achieved an accuracy of 0.864, demonstrating its effectiveness in staging the degree of liver fibrosis. [Extracted from the article]

Researchers from the University of Malaysia Perlis have developed a new method for detecting and segmenting the nucleus cell region in Pap smear images for cervical cancer screening. The proposed method, which includes color adjustment, k-means, and a modified Bradley algorithm, has been found to significantly and efficiently segment the nucleus cell region compared to the original Bradley algorithm. The researchers achieved high accuracy, sensitivity, and F-measure results, indicating the effectiveness of their approach. This research provides a new perspective on improving the convenience and accuracy of cervical cancer screening. [Extracted from the article]

Researchers from Regeneron Pharmaceuticals Inc. conducted a study on the application of multiple validated algorithms for identifying incident breast cancer among individuals with atopic dermatitis (AD). They tested two algorithms in multiple insurance claims databases and found that Algorithm 2 provided similar estimates to those of the Surveillance, Epidemiology, and End Results (SEER) program. However, they noted that the replicability of these algorithms in different data sources or subpopulations is rarely tested, and caution should be exercised when using them to calculate incidence or prevalence estimates. The study was supported by Sanofi and published in Pharmacoepidemiology and Drug Safety. [Extracted from the article]

A recent study conducted by researchers at Shanghai University of Electric Power explores the use of privacy-preserving algorithms in breast cancer prediction. The researchers highlight the need for pre-diagnosis testing to alleviate the strain on healthcare resources. They propose a highly optimized two-party logistic regression algorithm based on the CKKS scheme, which aims to enhance the accuracy and efficiency of pre-diagnosis testing while upholding data privacy. Simulation experiments demonstrate that the model's prediction accuracy exceeds 96% for two-sided encrypted data. This research contributes to the field of machine learning in healthcare and offers potential solutions for collaborative training of models while protecting private data. [Extracted from the article]

A research study conducted in Bucharest, Romania, analyzed the management of ectopic mediastinal thyroid (EMT) in relation to cancer and non-malignant findings. The study reviewed 89 original articles and classified them into four groups: studies/case series, malignant EMTs, benign EMTs without thyroid anomalies, and benign EMTs with thyroid anomalies. The researchers proposed a 10-item algorithm to navigate the EMT domain. The study found that the clinical index of suspicion for EMT remains low, but the rate of cancer is higher than previously reported. Surgery was found to have a good overall outcome. [Extracted from the article]

A recent report from the North China University of Science and Technology discusses the use of the Internet of Medical Things (IoMT) in cancer diagnosis and treatment. The report introduces a new algorithm called Dependent Nearest Neighbor Powered Twin Support Vector Machine (DTWSVM) for predicting cancer recurrence. The algorithm achieved an average accuracy of 91.53% across seven cancer datasets, outperforming other existing models. The researchers suggest that the DTWSVM algorithm could improve prediction accuracy and assist clinicians in making treatment decisions. [Extracted from the article]

Researchers at Fuzhou University in Fujian, China have developed a deep learning model fusion algorithm for the diagnosis of gastric mucosa-associated lymphoid tissue (MALT) lymphoma. The algorithm uses hematoxylin-eosin (H&E) whole slide images (WSIs) to accurately diagnose MALT lymphoma, which is a type of malignant tumor originating from the lymphohematopoietic system. The proposed framework achieved an accuracy of 98.53% using image patches and 94.96% on 258 WSIs, demonstrating its potential use in clinical practice. The research was funded by the National Natural Science Foundation of China and the Fujian Health Commission. [Extracted from the article]

New research from the University of Freiburg explores the estimation of blood pressure using image-free, wearable ultrasound technology. The study highlights the importance of monitoring cardiovascular health, as high blood pressure is a significant risk factor for cardiovascular diseases. The researchers developed algorithms and mathematical models to accurately extract blood pressure measurements from ultrasound data. The study validated these algorithms in silico, in vitro, and ex vivo, achieving promising results. Further research will involve in vivo measurements to confirm the accuracy of the device. [Extracted from the article]

Researchers at Shaqra University in Saudi Arabia have developed a new method for detecting and quantifying Breast Arterial Calcification (BAC) in mammograms. The method combines Enhanced Wolf Pack Algorithm (EWPA) for feature selection and Dense-kUNet segmentation for image classification. The proposed technique achieved high precision, recall, F-measure, and accuracy when compared to other methods on the Digital Database for Screening Mammography (DDSM). This research provides a promising approach to improve breast cancer screening and diagnosis. [Extracted from the article]

A new research study from the Autonomous University in Morelos, Mexico, has developed an improved object detection algorithm for breast cancer screening. The algorithm combines the Khalil conformable derivative and the Hessian matrix to enhance image quality and accuracy in identifying breast cancer. The research found that the algorithm provides better visual identification of conditions such as breast cancer, calcifications, and cerebral arteriovenous malformations, improving the accuracy of clinical diagnosis and subsequent monitoring. The study has been peer-reviewed and offers potential applications in medical image processing and analysis. [Extracted from the article]

A recent study conducted by researchers at George Emil Palade University of Medicine in Targu Mures, Romania, has developed a second trimester prediction algorithm for early-onset hypertensive disorders of pregnancy (HDP). The algorithm combines biochemical markers, such as the sFlt-1/PlGF ratio, with uterine artery Doppler ultrasound parameters to accurately predict the occurrence and severity of HDP in high-risk pregnant women. The study found that the combination of these markers improved the prediction of severe preeclampsia and its complications compared to using the markers or ultrasound parameters alone. Implementing these algorithms could enhance the accuracy of HDP screening and help manage maternal and fetal complications. [Extracted from the article]

Researchers from the College of Animal Sciences and Technology in Tongliao, China have developed a novel machine learning method for the early detection of brain tumors. The method combines the advantages of Gated Recurrent Unit (GRU) networks and the Enhanced Hybrid Dwarf Mongoose Optimization (EHDMO) algorithm to analyze MRI images and accurately detect brain tumors. The proposed method achieved high sensitivity, specificity, positive predictive value, negative predictive value, and accuracy, outperforming other recent methods. This research provides a promising approach to automate and improve brain tumor detection using advanced technology. [Extracted from the article]

Researchers from Hebei University in Baoding, China have developed a new algorithm for diagnosing neuroblastoma, a type of malignant solid tumor in children. The algorithm uses a network model based on the U-Net structure, with DenseNet as the encoder, to extract features from whole slide images of neuroblastoma. The algorithm achieved high accuracy in segmenting and counting poorly differentiated and differentiated cells, which can help determine the histopathological types and prognostic effects of neuroblastoma. The research has been peer-reviewed and published in the journal Signal Image and Video Processing. [Extracted from the article]

Insilico Medicine, an AI-driven biotech company, has developed a groundbreaking algorithm called Quantum-assisted Fragment-based Automated Structure Generator (QFASG) that aids in automated drug design. QFASG uses advanced chemoinformatic techniques to create new small-molecule structures from a library of molecular fragments. The researchers successfully applied QFASG to generate new small-molecule inhibitors for two pharmacological targets involved in cancer pathogenesis. The algorithm's fully automated pipeline streamlines the process of structure generation and optimization, providing researchers with a powerful tool for accelerating the drug discovery process. [Extracted from the article]

A recent report from the Department of Electrical and Computer Engineering discusses the design and simulation of an edge compute architecture for an IoT-based clinical decision support system (CDSS). CDSS has transformed healthcare by utilizing technologies such as IoT, AI, and predictive analysis. The report highlights the drawbacks of cloud-based monitoring systems in healthcare and proposes the use of edge computing to overcome latency issues. The research demonstrates that edge computing reduces decision latency by approximately 87 times and network utilization by 1.5 times, showing its efficacy in implementing IoT-based CDSS. [Extracted from the article]

Researchers at Lovely Professional University in Phagwara, India have developed a hybrid feature selection technique to improve the speed and performance of machine learning models in diagnosing breast cancer. The technique combines Regularized Neighborhood Component Analysis (RNCA) and Binary Teaching Learning Based Optimization (BTLBO) algorithms to select the best features for classification. The results of the study showed that the hybrid model outperformed other feature selection methods in terms of classification accuracy, recall, and AUC measures. This research has been peer-reviewed and published in The International Arab Journal of Information Technology. [Extracted from the article]

A clinical trial, NCT06283524, is being conducted to develop a non-invasive eye-tracking software application for multiple sclerosis (MS) patients. The trial aims to use eye-tracking technology to collect data on eye movements and cognitive function in MS patients. Machine learning algorithms will be trained to identify eye-tracking metrics that can serve as markers of MS disease severity and cognitive status. The trial will recruit 100 MS patients with varying levels of disability and will take place in Canada. The estimated completion date for the study is March 18, 2026. [Extracted from the article]

A recent study conducted at Anhui University in China has proposed a new algorithm called the adaptive conjugate gradient accelerated evolutionary algorithm (ACG-EA) for optimizing cancer treatment plans in intensity-modulated proton therapy. This technique is considered one of the most advanced cancer treatment methods due to the unique characteristics of proton radiation. The ACG-EA algorithm aims to generate diverse high-quality treatment plans simultaneously, addressing the limitations of existing optimization methods. Experimental results have shown promising performance in six clinical cancer cases. This research has been peer-reviewed and published in the journal Applied Soft Computing. [Extracted from the article]

A study conducted by the ACECR Institute of Higher Education in Isfahan, Iran, aimed to improve the performance of the Computer-Aided Detection (CAD) system for breast cancer detection using thermal images. The researchers developed an intelligent method that involved feature extraction, feature selection, classification, and artificial intelligence techniques. The method was evaluated using breast thermograms from 200 subjects, and the results showed high accuracy, sensitivity, and specificity when using different classifier algorithms. This study demonstrates the effectiveness of the intelligent method in improving the accuracy of breast cancer detection. [Extracted from the article]

A consortium led by scientists from the German Cancer Research Center (DKFZ) and the National Center for Tumor Diseases (NCT) has developed an online tool called "Metrics Reloaded" to help users select the most suitable algorithm for AI-supported image analysis. The tool provides information on the strengths, weaknesses, and limitations of different validation metrics used to evaluate the performance of algorithms in medical image analysis. The researchers aim to improve the quality and reliability of AI-supported image analysis results, promoting confidence in routine clinical practice. The tool is applicable to various categories of image analysis problems and can be used for different types of medical images as well as other areas of image analysis. [Extracted from the article]

Researchers from HITEC University in Taxila, Pakistan have developed a framework for classifying brain tumors using MRI scans. The framework utilizes a hybrid deep learning model and an optimal information fusion framework to improve accuracy. The researchers addressed the challenge of imbalanced training data by using a sparse autoencoder network to generate new images. They also utilized Bayesian optimization to initialize the hyperparameters of two pretrained neural networks. The proposed framework achieved an accuracy of 99.80% and showed improvement compared to existing methods. [Extracted from the article]

A research study conducted at GLA University in Mathura, India, explores the use of metaheuristic feature selection algorithms to improve the accuracy of machine learning models for breast cancer classification. The study introduces a hybrid approach that combines the Gravitational Search Optimization Algorithm (GSOA) and Emperor Penguin Optimization (EPO) to identify influential features while eliminating irrelevant ones. The research aims to determine the minimum number of features needed to achieve higher accuracy in breast cancer classification. The experimental results demonstrate promising outcomes, with an accuracy of 97.66%, sensitivity of 0.9687, specificity of 1.000, precision of 1.000, F1-score of 0.9516, and area under the curve (AUC) of 0.9980. [Extracted from the article]

A recent report from the University College of Engineering in Nagercoil, India highlights the use of infrared thermography as an effective screening method for detecting breast cancer. The report discusses the application of deep learning algorithms in thermography to increase the accuracy of detection. The researchers propose an automated and optimized convolution neural network-based system for tumor segmentation of breast thermogram images, using a bio-inspired optimization technique called the mayfly algorithm. The results of the study suggest that this technique can assist physicians in making better decisions regarding breast cancer diagnosis. [Extracted from the article]

Researchers from Tehran University of Medical Sciences have developed a new approach for automating the segmentation of glioblastoma multiforme (GBM) tumors from MRI images. The technique utilizes the Marker-controlled watershed segmentation (MCWS) algorithm, along with other image processing techniques, to improve segmentation accuracy. The study highlights the importance of reliable and accurate image segmentation for effective diagnosis and treatment planning of GBM tumors. The MCWS technique is considered an effective and efficient approach for segmenting challenging medical images. [Extracted from the article]

A recent study conducted by researchers at the University of Sydney explored the adaptability of artificial intelligence (AI) models to the Australian context of mammography for breast cancer diagnosis. The study examined the impact of image enhancement on model performance and analyzed the relationship between AI outputs and histopathological features. The results showed that AI models performed better when trained on a subset of mammograms with high concordance between radiologists' annotations and when image enhancement techniques were applied. The study concluded that AI models can enhance the accuracy of breast cancer diagnosis when certain factors, such as concordance classification, image enhancement, and transfer learning, are taken into account. [Extracted from the article]

Researchers at the University of Science and Technology Beijing have developed a lightweight deep learning-based algorithm for segmenting bowel sounds (BS) in gastrointestinal monitoring. The algorithm utilizes one-dimensional convolution layers and bidirectional gate recurrent unit (GRU) layers to enhance feature extraction. A loss function called shape loss is proposed to reduce the sensitivity of the model to thresholds. Experimental results show that the proposed method outperforms other comparison methods in terms of f1-scores and sensitivity to thresholds. This research was supported by the National Natural Science Foundation of China. [Extracted from the article]

A study conducted by researchers at Chengdu University of Traditional Chinese Medicine in Sichuan, China, explores the use of artificial intelligence in diagnosing breast cancer. The researchers propose a new method that utilizes a modified ZFNet network and an extreme learning machine (ELM) to enhance classification performance. The study compares the performance of their method to other well-known methods using various metrics and concludes that their approach, known as ZFNet-SWChOA-ELM, demonstrates the highest level of performance. This research has been peer-reviewed and provides valuable insights into the use of evolving deep convolutional neural networks for breast cancer diagnosis. [Extracted from the article]

Researchers from Coimbatore, India have developed a two-phase deep learning-based framework to recognize and classify brain cancers in magnetic resonance images (MRIs). The first phase utilizes a new DTA approach to identify tumor MRI images from healthy individuals, while the second phase employs a hybrid features fusion-based brain tumor classification technique. The researchers evaluated several segmentation algorithms for MRI and PET scans, including Ant Colony Optimization, Wavelet Transform, Gradient Vector Flow, Gray level Co-occurrence matrix, and Artificial Bee Colony. The study found that the GVF strategy performed better with MRI images, while the ABC and ACO approaches performed similarly with PET scans. [Extracted from the article]