COVID-19 detection in lung CT slices using Brownian-butterfly-algorithm optimized lightweight deep features.

Several deep-learning assisted disease assessment schemes (DAS) have been proposed to enhance accurate detection of COVID-19, a critical medical emergency, through the analysis of clinical data. Lung imaging, particularly from CT scans, plays a pivotal role in identifying and assessing the severity of COVID-19 infections. Existing automated methods leveraging deep learning contribute significantly to reducing the diagnostic burden associated with this process.

A stroke prediction framework using explainable ensemble learning.

The death of brain cells occurs when blood flow to a particular area of the brain is abruptly cut off, resulting in a stroke. Early recognition of stroke symptoms is essential to prevent strokes and promote a healthy lifestyle. FAST tests (looking for abnormalities in the face, arms, and speech) have limitations in reliability and accuracy for diagnosing strokes.

Colon histology slide classification with deep-learning framework using individual and fused features.

Cancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recommended clinical protocol are necessary to trat cancer.

OralNet: Fused Optimal Deep Features Framework for Oral Squamous Cell Carcinoma Detection.

Humankind is witnessing a gradual increase in cancer incidence, emphasizing the importance of early diagnosis and treatment, and follow-up clinical protocols. Oral or mouth cancer, categorized under head and neck cancers, requires effective screening for timely detection. This study proposes a framework, OralNet, for oral cancer detection using histopathology images.

Brain Tumor Class Detection in Flair/T2 Modality MRI Slices Using Elephant-Herd Algorithm Optimized Features.

Several advances in computing facilities were made due to the advancement of science and technology, including the implementation of automation in multi-specialty hospitals. This research aims to develop an efficient deep-learning-based brain-tumor (BT) detection scheme to detect the tumor in FLAIR- and T2-modality magnetic-resonance-imaging (MRI) slices. MRI slices of the axial-plane brain are used to test and verify the scheme.