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Brain tumor has become one of the fatal causes of death worldwide in recent years, affecting many individuals annually and resulting in loss of lives. Brain tumors are characterized by the abnormal or irregular growth of brain tissues that can spread to nearby tissues and eventually throughout the brain. Although several traditional machine learning and deep learning techniques have been developed for detecting and classifying brain tumors, they do not always provide an accurate and timely diagnosis. This study proposes a conditional generative adversarial network (CGAN) that leverages the fine-tuning of a convolutional neural network (CNN) to achieve more precise detection of brain tumors. The CGAN comprises two parts, a generator and a discriminator, whose outputs are used as inputs for fine-tuning the CNN model. The publicly available dataset of brain tumor MRI images on Kaggle was used to conduct experiments for Datasets 1 and 2. Statistical values such as precision, specificity, sensitivity, F1-score, and accuracy were used to evaluate the results. Compared to existing techniques, our proposed CGAN model achieved an accuracy value of 0.93 for Dataset 1 and 0.97 for Dataset 2. [ABSTRACT FROM AUTHOR]

Leukemia is a kind of blood cancer that damages the cells in the blood and bone marrow of the human body. It produces cancerous blood cells that disturb the human's immune system and significantly affect bone marrow's production ability to effectively create different types of blood cells like red blood cells (RBCs) and white blood cells (WBC), and platelets. Leukemia can be diagnosed manually by taking a complete blood count test of the patient's blood, from which medical professionals can investigate the signs of leukemia cells. Furthermore, two other methods, microscopic inspection of blood smears and bone marrow aspiration, are also utilized while examining the patient for leukemia. However, all these methods are labor-intensive, slow, inaccurate, and require a lot of human experience and dedication. Different authors have proposed automated detection systems for leukemia diagnosis to overcome these limitations. They have deployed digital image processing and machine learning algorithms to classify the cells into normal and blast cells. However, these systems are more efficient, reliable, and fast than previous manual diagnosing methods. However, more work is required to classify leukemia-affected cells due to the complex characteristics of blood images and leukemia cells having much intra-class variability and inter-class similarity. In this paper, we have proposed a robust automated system to diagnose leukemia and its sub-types. We have classified ALL into its sub-types based on FAB classification, i.e., L1, L2, and L3 types with better performance. We have achieved 96.06% accuracy for subtypes classification, which is better when compared with the state-of-the-art methodologies. [ABSTRACT FROM AUTHOR]

A brain tumor is a mass or growth of abnormal cells in the brain. In children and adults, brain tumor is considered one of the leading causes of death. There are several types of brain tumors, including benign (non-cancerous) and malignant (cancerous) tumors. Diagnosing brain tumors as early as possible is essential, as this can improve the chances of successful treatment and survival. Considering this problem, we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models (Resnet50, Vgg16, Vgg19, U-Net) and their integration for computer-aided detection and localization systems in brain tumors. These pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome Atlas. The dataset consists of 120 patients. The pre-trained models have been used to classify tumor or no tumor images, while integrated models are applied to segment the tumor region correctly. We have evaluated their performance in terms of loss, accuracy, intersection over union, Jaccard distance, dice coefficient, and dice coefficient loss. From pre-trained models, the U-Net model achieves higher performance than other models by obtaining 95% accuracy. In contrast, U-Net with ResNet-50 outperforms all other models from integrated pre-trained models and correctly classified and segmented the tumor region. [ABSTRACT FROM AUTHOR]

The brain tumor is an abnormal and hysterical growth of brain tissues, and the leading cause of death affected patients worldwide. Even in this technology-based arena, brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original ones. The artificially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented data. To overcome the highlighted issue, a Generative Adversarial Network (GAN) deep learning technique in which two neural networks compete to become more accurate in creating artificially realistic data for MRI images. The GAN network contains mainly two parts known as generator and discriminator. Commonly, a generator is the convolutional neural network, and a discriminator is the deconvolutional neural network. In this research, the publicly accessible Contrast-Enhanced Magnetic Resonance Imaging (CE-MRI) dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been used. Our proposed method is simple and achieved an accuracy of 96%. We compare our technique results with the existing results, indicating that our proposed technique outperforms the best results associated with the existing methods. [ABSTRACT FROM AUTHOR]

Human brain consists of millions of cells to control the overall structure of the human body. When these cells start behaving abnormally, then brain tumors occurred. Precise and initial stage brain tumor detection has always been an issue in the field of medicines for medical experts. To handle this issue, various deep learning techniques for brain tumor detection and segmentation techniques have been developed, which worked on different datasets to obtain fruitful results, but the problem still exists for the initial stage of detection of brain tumors to save human lives. For this purpose, we proposed a novel U-Net-based Convolutional Neural Network (CNN) technique to detect and segmentizes the brain tumor for Magnetic Resonance Imaging (MRI). Moreover, a 2-dimensional publicly available Multimodal Brain Tumor Image Segmentation (BRATS2020) dataset with 1840 MRI images of brain tumors has been used having an image size of 240×240 pixels. After initial dataset preprocessing the proposed model is trained by dividing the dataset into three parts i.e., testing, training, and validation process. Our model attained an accuracy value of 0.98%on the BRATS2020 dataset, which is the highest one as compared to the already existing techniques. [ABSTRACT FROM AUTHOR]

Today, liver disease, or any deterioration in one's ability to survive, is extremely common all around the world. Previous research has indicated that liver disease is more frequent in younger people than in older ones. When the liver's capability begins to deteriorate, life can be shortened to one or two days, and early prediction of such diseases is difficult. Using several machine learning (ML) approaches, researchers analyzed a variety of models for predicting liver disorders in their early stages. As a result, this research looks at using the Random Forest (RF) classifier to diagnose the liver disease early on. The dataset was picked from the University of California, Irvine repository. RF's accomplishments are contrasted to those of Multi-Layer Perceptron (MLP), Average One Dependency Estimator (A1DE), Support Vector Machine (SVM), Credal Decision Tree (CDT), Composite Hypercube on Iterated Random Projection (CHIRP), K-nearest neighbor (KNN), Naïve Bayes (NB), J48-Decision Tree (J48), and Forest by Penalizing Attributes (Forest-PA). Some of the assessment measures used to evaluate each classifier include Root Relative Squared Error (RRSE), Root Mean Squared Error (RMSE), accuracy, recall, precision, specificity, Matthew's Correlation Coefficient (MCC), F-measure, and G-measure. RF has an RRSE performance of 87.6766 and an RMSE performance of 0.4328, however, its percentage accuracy is 72.1739. The widely acknowledged result of this work can be used as a starting point for subsequent research. As a result, every claim that a new model, framework, or method enhances forecastingmay be benchmarked and demonstrated. [ABSTRACT FROM AUTHOR]

Coronavirus (COVID-19 or SARS-CoV-2) is a novel viral infection that started in December 2019 and has erupted rapidly in more than 150 countries. The rapid spread of COVID-19 has caused a global health emergency and resulted in governments imposing lock-downs to stop its transmission. There is a significant increase in the number of patients infected, resulting in a lack of test resources and kits in most countries. To overcome this panicked state of affairs, researchers are looking forward to some effective solutions to overcome this situation: one of the most common and effective methods is to examine the X-radiation (X-rays) and computed tomography (CT) images for detection of Covid-19. However, this method burdens the radiologist to examine each report. Therefore, to reduce the burden on the radiologist, an effective, robust and reliable detection system has been developed, which may assist the radiologist and medical specialist in effective detecting of COVID. We proposed a deep learning approach that uses readily available chest radio-graphs (chest X-rays) to diagnose COVID-19 cases. The proposed approach applied transfer learning to the Deep Convolutional Neural Network (DCNN) model, Inception-v4, for the automatic detection of COVID-19 infection from chest X-rays images. The dataset used in this study contains 1504 chest X-ray images, 504 images of COVID-19 infection, and 1000 normal images obtained from publicly available medical repositories. The results showed that the proposed approach detected COVID-19 infection with an overall accuracy of 99.63%. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the most prominent sources of death in females. Every year many women suffer breast cancer, and, in the end, death occurs. The early detection of breast cancer may cause to reduce the death rate and save women's lives. The medical care and cost of prevention of women's breast cancer are costly and become a priority to diagnose breast cancer at its early stages. Initially, the mammography technique was the leading technique to detect the early stage of breast cancer. However, it cannot deal with a tumor size of less than 2 mm. To overcome this challenge, by considering the DMR-thermogram images, a novel layer-based Convolutional Neural Network (BreastCNN) for breast cancer detection and classification was proposed. BreastCNN method works in five different layers and uses different types of filters. The learning rate and structures of layers change after every convolution layer. The proposed technique is tested on the Database for Mastology Research (DMR) having 745 healthy and 261 sick images. The performance is calculated as the statistical values known as sensitivity, specificity, precision, accuracy, and F1-score. The proposed technique shows better accuracy of 99.7% as related to the already presented methods. [ABSTRACT FROM AUTHOR]