Brain tumor detection and classification in MRI using hybrid ViT and GRU model with explainable AI in Southern Bangladesh.

Brain tumor, a leading cause of uncontrolled cell growth in the central nervous system, presents substantial challenges in medical diagnosis and treatment. Early and accurate detection is essential for effective intervention. This study aims to enhance the detection and classification of brain tumors in Magnetic Resonance Imaging (MRI) scans using an innovative framework combining Vision Transformer (ViT) and Gated Recurrent Unit (GRU) models.

Enhancing and improving the performance of imbalanced class data using novel GBO and SSG: A comparative analysis.

Class imbalance problem (CIP) in a dataset is a major challenge that significantly affects the performance of Machine Learning (ML) models resulting in biased predictions. Numerous techniques have been proposed to address CIP, including, but not limited to, Oversampling, Undersampling, and cost-sensitive approaches. Due to its ability to generate synthetic data, oversampling techniques such as the Synthetic Minority Oversampling Technique (SMOTE) are the most widely used methodology by researchers.

Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks.

Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colon cancer is made through histological examination. Convolutional neural networks are being used to analyze colon cancer via digital image processing with the introduction of whole-slide imaging.

Deep transfer learning approaches for Monkeypox disease diagnosis.

Monkeypox has become a significant global challenge as the number of cases increases daily. Those infected with the disease often display various skin symptoms and can spread the infection through contamination. Recently, Machine Learning (ML) has shown potential in image-based diagnoses, such as detecting cancer, identifying tumor cells, and identifying coronavirus disease (COVID)-19 patients.