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Skin cancer is a significant healthcare problem with a high mortality rate worldwide. Skin lesions occur due to the abnormal growth of skin cells in humans. Failure of early prediction and proper lesion diagnosis may lead to a malignant stage. In recent times, different skin lesion images have appeared with high similarity. Hence, classification is a more challenging task with imbalances in the dataset. The proposed work is implemented as a hybrid model with a dynamic salp swarm algorithm (DSSA) with a weighted extreme learning machine (DSSA-WELM) that addresses the imbalances in the dataset and performs the classification with higher accuracy. GoogleNet is a pre-trained network model used with the hybrid model, which helps converge faster with the optimization process. The extreme learning machine (ELM) is a multiclass classifier for accurate dermoscopic image classification. The DSSA, the best feature selection algorithm enhances the classification accuracy of the WELM. Image classification is accomplished with the International Skin Imaging Collaboration 2019 benchmark dataset. The proposed solution classifies images into eight classes: melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, dermatofibroma, vascular lesion, squamous cell carcinoma and unknown lesion. The efficiency of the proposed solution is proved by comparing it with various state-of-the-art approaches such as support vector machine (SVM), ELM, and particle swarm optimization (PSO) methods. Results are evaluated using standard metrics of sensitivity, specificity and precision. The proposed solution outperforms all these older approaches.

Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, the advent of deep learning techniques has showcased notable potential in elevating the precision and efficiency of histopathological data analysis. The proposed work introduces a novel approach that harnesses the power of Transfer Learning to capitalize on knowledge gleaned from pre-trained models, adapting it to the nuanced landscape of breast cancer histopathology. Our proposed model, a Transfer Learning-based concatenated model, exhibits substantial performance enhancements compared to traditional methodologies. Leveraging well-established pretrained models such as VGG-16, MobileNetV2, ResNet50, and DenseNet121—each Convolutional Neural Network architecture designed for classification tasks—this study meticulously tunes hyperparameters to optimize model performance. The implementation of a concatenated classification model is systematically benchmarked against individual classifiers on histopathological data. Remarkably, our concatenated model achieves an impressive training accuracy of 98%. The outcomes of our experiments underscore the efficacy of this four-level concatenated model in advancing the accuracy of breast cancer histopathological data analysis. By synergizing the strengths of deep learning and transfer learning, our approach holds the potential to augment the diagnostic capabilities of pathologists, thereby contributing to more informed and personalized treatment planning for individuals diagnosed with breast cancer. This research heralds a promising stride toward leveraging cutting-edge technology to refine the understanding and management of breast cancer, marking a significant advancement in the intersection of artificial intelligence and healthcare.

Cardiovascular diseases, prevalent as leading health concerns, demand early diagnosis for effective risk prevention. Despite numerous diagnostic models, challenges persist in network configuration and performance degradation, impacting model accuracy. In response, this paper introduces the Optimally Configured and Improved Long Short-Term Memory (OCI-LSTM) model as a robust solution. Leveraging the Salp Swarm Algorithm, irrelevant features are systematically eliminated, and the Genetic Algorithm is employed to optimize the LSTM’s network configuration. Validation metrics, including the accuracy, sensitivity, specificity, and F1 score, affirm the model’s efficacy. Comparative analysis with a Deep Neural Network and Deep Belief Network establishes the OCI-LSTM’s superiority, showcasing a notable accuracy increase of 97.11%. These advancements position the OCI-LSTM as a promising model for accurate and efficient early diagnosis of cardiovascular diseases. Future research could explore real-world implementation and further refinement for seamless integration into clinical practice.

Convolutional neural networks (CNNs) have been extensively utilized in medical image processing to automatically extract meaningful features and classify various medical conditions, enabling faster and more accurate diagnoses. In this paper, LeNet, a classic CNN architecture, has been successfully applied to breast cancer data analysis. It demonstrates its ability to extract discriminative features and classify malignant and benign tumors with high accuracy, thereby supporting early detection and diagnosis of breast cancer. LeNet with corrected Rectified Linear Unit (ReLU), a modification of the traditional ReLU activation function, has been found to improve the performance of LeNet in breast cancer data analysis tasks via addressing the “dying ReLU” problem and enhancing the discriminative power of the extracted features. This has led to more accurate, reliable breast cancer detection and diagnosis and improved patient outcomes. Batch normalization improves the performance and training stability of small and shallow CNN architecture like LeNet. It helps to mitigate the effects of internal covariate shift, which refers to the change in the distribution of network activations during training. This classifier will lessen the overfitting problem and reduce the running time. The designed classifier is evaluated against the benchmarking deep learning models, proving that this has produced a higher recognition rate. The accuracy of the breast image recognition rate is 89.91%. This model will achieve better performance in segmentation, feature extraction, classification, and breast cancer tumor detection.

Background: Breast cancer is one of the most frequent types of cancer among women and early identification can reduce the mortality rate drastically. Feature selection is one of the significant tasks in the breast cancer analysis process. Several types of feature selection algorithms have been implemented to select the most appropriate feature for breast cancer analysis. However, they have to take a longer time to converge, over-fitting problems and providing less accuracy. Hence, a hybrid bat optimization algorithm combined with chaotic maps and fuzzy C-means clustering algorithm (BSCFC) is proposed for feature selection. Aims and Objectives: An integrated optimized bat optimization algorithm combined with chaotic maps and fuzzy C-means clustering algorithm (BSCFC) is proposed to determine the relevant feature. Materials and Methods: Breast cancer mini-Mammographic Image Analysis Society database (MIAS) dataset is used for analysis. Further, median filters are used for preprocessing, Region of Interest (ROI) was utilized for segmentation, gray level co-occurrence matrix (GLCM), and texture analysis are utilized in the feature extraction process. A hybrid bat optimization algorithm combined with chaotic maps and fuzzy C-means clustering algorithm (BSCFC) is proposed for feature selection. K nearest neighbor (KNN) classifier is used for classification. Results: Performance of the proposed system is evaluated using standard measures and achieved the highest accuracy rate of (98.2%), specificity of (97.3%), and sensitivity of (98.3%) as compared to other relevant methods such as bat, chaotic bat, chaotic crow search, ant lion optimization, and chaotic ant lion optimization algorithm. Conclusion: The proposed BSCFC algorithm is designed to improve the performance of convergence speed and control balance between exploration and exploitation rate using five types of chaotic maps namely sinusoidal, sine, gauss, logistic, and tent maps. The results show that the BSCFC with sinusoidal maps can significantly boost the classification performance of the BSCFC algorithm in classifying the breast cancer images with reduced features, which in turn optimizes the radiologists' time for their interpretation.

Background: Cardiac arrhythmias are one of the leading causes of heart failure. In particular, atrial fibrillation (AFib) is a kind of arrhythmia that can lead to heart stroke and myocardial infarction. It is very important and crucial to predict AFib at an early stage to prevent heart disease. Electrocardiogram is one of the premium diagnostic tools which is used by most of the researchers for predicting irregular heartbeats. There are many works carried out in finding heart disease using machine learning classifiers. Aims and Objectives: Deep learning based hybrid Long Short Term Memory (LSTM) network is hybridized with Enhanced Whale Optimization (EWO) to minimize the network optimization and configuration issues faced in the existing models and proposed to increases the accuracy of predicting AFib. Materials and Methods: The proposed LSTM network is hybridized with a EWO technique for predicting AFib. This study uses a hybrid LSTM EWO network for classifying the various output labels of heart disease. EWO is used to predict the most relevant features from the raw dataset. Then, the LSTM model is used to predict the AFib of a patient from normal ECG data. Results: The DL based LSTM EWO achieves better results in all the performance metrics by analyzing the optimized features in feature space, training, and testing phase and successfully obtains better performance in an effective manner. LSTM improves the accuracy by reducing the number of units in the hidden layer which optimizes the network configuration. The proposed model achieves 96.12% accuracy which is 12.81% higher than RF, 15.01% higher than GB, 28.04% higher than CART, and 16.92% higher than SVM. Conclusion: The proposed model hybrid LSTM network integrated EWO for predicting the AFib. The EWO is applied for selecting the most appropriate features needed for the model to learn and produce improvised performance. The optimization and network configuration problems faced in the existing studies are avoided by choosing the suitable number of LSTM units and the size of the time window. This has been implemented through LSTM units and their window size. In addition, we made a statistical examination to prove the importance of proposed work against other models. It is observed that the experimental results attained with 96% of accuracy, better than conventional models.

In recent years, predictive data mining techniques play a vital role in the field of medical informatics. These techniques help the medical practitioners in predicting various classes which is useful in prediction treatment. One of such major difficulty is prediction of survival rate in breast cancer patients. Breast cancer is a common disease these days and fighting against it is a tough battle for both the surgeons and the patients. To predict the survivability rate in breast cancer patients which helps the medical practitioner to select the type of treatment a predictive data mining technique called Diversified Multiple Decision Tree (DMDT) classification is used. Additionally, to avoid difficulties from the outlier and skewed data, it is also proposed to perform the improvement of training space by outlier filtering and over sampling. As a result, this novel approach gives the survivability rate of the cancer patients based on which the medical practitioners can choose the type of treatment.