An attention-based deep learning for acute lymphoblastic leukemia classification.

The bone marrow overproduces immature cells in the malignancy known as Acute Lymphoblastic Leukemia (ALL). In the United States, about 6500 occurrences of ALL are diagnosed each year in both children and adults, comprising nearly 25% of pediatric cancer cases. Recently, many computer-assisted diagnosis (CAD) systems have been proposed to aid hematologists in reducing workload, providing correct results, and managing enormous volumes of data. Traditional CAD systems rely on hematologists' expertise, specialized features, and subject knowledge. Utilizing early detection of ALL can aid radiologists and doctors in making medical decisions. In this study, Deep Dilated Residual Convolutional Neural Network (DDRNet) is presented for the classification of blood cell images, focusing on eosinophils, lymphocytes, monocytes, and neutrophils. To tackle challenges like vanishing gradients and enhance feature extraction, the model incorporates Deep Residual Dilated Blocks (DRDB) for faster convergence. Conventional residual blocks are strategically placed between layers to preserve original information and extract general feature maps. Global and Local Feature Enhancement Blocks (GLFEB) balance weak contributions from shallow layers for improved feature normalization. The global feature from the initial convolution layer, when combined with GLFEB-processed features, reinforces classification representations. The Tanh function introduces non-linearity. A Channel and Spatial Attention Block (CSAB) is integrated into the neural network to emphasize or minimize specific feature channels, while fully connected layers transform the data. The use of a sigmoid activation function concentrates on relevant features for multiclass lymphoblastic leukemia classification The model was analyzed with Kaggle dataset (16,249 images) categorized into four classes, with a training and testing ratio of 80:20. Experimental results showed that DRDB, GLFEB and CSAB blocks' feature discrimination ability boosted the DDRNet model F1 score to 0.96 with minimal computational complexity and optimum classification accuracy of 99.86% and 91.98% for training and testing data. The DDRNet model stands out from existing methods due to its high testing accuracy of 91.98%, F1 score of 0.96, minimal computational complexity, and enhanced feature discrimination ability. The strategic combination of these blocks (DRDB, GLFEB, and CSAB) are designed to address specific challenges in the classification process, leading to improved discrimination of features crucial for accurate multi-class blood cell image identification. Their effective integration within the model contributes to the superior performance of DDRNet.

ALNett: A cluster layer deep convolutional neural network for acute lymphoblastic leukemia classification.

Acute Lymphoblastic Leukemia (ALL) is cancer in which bone marrow overproduces undeveloped lymphocytes. Over 6500 cases of ALL are diagnosed every year in the United States in both adults and children, accounting for around 25% of pediatric cancers, and the trend continues to rise. With the advancements of AI and big data analytics, early diagnosis of ALL can be used to aid the clinical decisions of physicians and radiologists. This research proposes a deep neural network-based (ALNett) model that employs depth-wise convolution with different dilation rates to classify microscopic white blood cell images. Specifically, the cluster layers encompass convolution and max-pooling followed by a normalization process that provides enriched structural and contextual details to extract robust local and global features from the microscopic images for the accurate prediction of ALL. The performance of the model was compared with various pre-trained models, including VGG16, ResNet-50, GoogleNet, and AlexNet, based on precision, recall, accuracy, F1 score, loss accuracy, and receiver operating characteristic (ROC) curves. Experimental results showed that the proposed ALNett model yielded the highest classification accuracy of 91.13% and an F1 score of 0.96 with less computational complexity. ALNett demonstrated promising ALL categorization and outperformed the other pre-trained models.

CovMnet-Deep Learning Model for classifying Coronavirus (COVID-19).

Diagnosing COVID-19, current pandemic disease using Chest X-ray images is widely used to evaluate the lung disorders. As the spread of the disease is enormous many medical camps are being conducted to screen the patients and Chest X-ray is a simple imaging modality to detect presence of lung disorders. Manual lung disorder detection using Chest X-ray by radiologist is a tedious process and may lead to inter and intra-rate errors. Various deep convolution neural network techniques were tested for detecting COVID-19 abnormalities in lungs using Chest X-ray images. This paper proposes deep learning model to classify COVID-19 and normal chest X-ray images. Experiments are carried out for deep feature extraction, fine-tuning of convolutional neural networks (CNN) hyper parameters, and end-to-end training of four variants of the CNN model. The proposed CovMnet provide better classification accuracy of 97.4% for COVID-19 /normal than those reported in the previous studies. The proposed CovMnet model has potential to aid radiologist to monitor COVID-19 disease and proves to be an efficient non-invasive COVID-19 diagnostic tool for lung disorders.

Machine learning approach for anxiety and sleep disorders analysis during COVID-19 lockdown.

The Severe Acute Respiratory Syndrome (SARS)-CoV-2 virus caused COVID-19 pandemic has led to various kinds of anxiety and stress in different strata and sections of the society. The aim of this study is to analyse the sleeping and anxiety disorder for a wide distribution of people of different ages and from different strata of life. The study also seeks to investigate the different symptoms and grievances that people suffer from in connection with their sleep patterns and predict the possible relationships and factors in association with outcomes related to COVID-19 pandemic induced stress and issues. A total of 740 participants (51.3% male and 48.7% female) structured with 2 sections, first with general demographic information and second with more targeted questions for each demographic were surveyed. Pittsburgh Sleep Quality Index (PSQI) and General Anxiety Disorder assessment (GAD-7) standard scales were utilized to measure the stress, sleep disorders and anxiety. Experimental results showed positive correlation between PSQI and GAD-7 scores for the participants. After adjusting for age and gender, occupation does not have an effect on sleep quality (PSQI), but it does have an effect on anxiety (GAD-7). Student community in spite of less susceptible to COVID-19 infection found to be highly prone to psychopathy mental health disturbances during the COVID-19 pandemic. The study also highlights the connectivity between lower social status and mental health issues. Random Forest model for college students indicates clearly the stress induced factors as anxiety score, worry about inability to understand concepts taught online, involvement of parents, college hours, worrying about other work load and deadlines for the young students studying in Universities.

Computer-aided diagnosis of COVID-19 from chest X-ray images using histogram-oriented gradient features and Random Forest classifier.

The decision-making process is very crucial in healthcare, which includes quick diagnostic methods to monitor and prevent the COVID-19 pandemic disease from spreading. Computed tomography (CT) is a diagnostic tool used by radiologists to treat COVID patients. COVID x-ray images have inherent texture variations and similarity to other diseases like pneumonia. Manually diagnosing COVID X-ray images is a tedious and challenging process. Extracting the discriminant features and fine-tuning the classifiers using low-resolution images with a limited COVID x-ray dataset is a major challenge in computer aided diagnosis. The present work addresses this issue by proposing and implementing Histogram Oriented Gradient (HOG) features trained with an optimized Random Forest (RF) classifier. The proposed HOG feature extraction method is evaluated with Gray-Level Co-Occurrence Matrix (GLCM) and Hu moments. Results confirm that HOG is found to reflect the local description of edges effectively and provide excellent structural features to discriminate COVID and non-COVID when compared to the other feature extraction techniques. The performance of the RF is compared with other classifiers such as Linear Regression (LR), Linear Discriminant Analysis (LDA), K-nearest neighbor (kNN), Classification and Regression Trees (CART), Random Forest (RF), Support Vector Machine (SVM), and Multi-layer perceptron neural network (MLP). Experimental results show that the highest classification accuracy (99. 73%) is achieved using HOG trained by using the Random Forest (RF) classifier. The proposed work has provided promising results to assist radiologists/physicians in automatic COVID diagnosis using X-ray images.