An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study.

Computer-aided diagnosis (CAD) systems play a vital role in modern research by effectively minimizing both time and costs. These systems support healthcare professionals like radiologists in their decision-making process by efficiently detecting abnormalities as well as offering accurate and dependable information. These systems heavily depend on the efficient selection of features to accurately categorize high-dimensional biological data.

Efficient feature selection based novel clinical decision support system for glaucoma prediction from retinal fundus images.

The process of feature selection (FS) is vital aspect of machine learning (ML) model's performance enhancement where the objective is the selection of the most influential subset of features. This paper suggests the Gravitational search optimization algorithm (GSOA) technique for metaheuristic-based FS. Glaucoma disease is selected as the subject of investigation as this disease is spreading worldwide at a very fast pace; 111 million instances of glaucoma are expected by 2040, up from 64 million in 2015.

Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images.

Glaucoma is the dominant reason for irreversible blindness worldwide, and its best remedy is early and timely detection. Optical coherence tomography has come to be the most commonly used imaging modality in detecting glaucomatous damage in recent years. Deep Learning using Optical Coherence Tomography Modality helps in predicting glaucoma more accurately and less tediously.

Breast cancer prediction using a hybrid method based on Butterfly Optimization Algorithm and Ant Lion Optimizer.

The design and development of a computer-based system for breast cancer detection are largely reliant on feature selection techniques. These techniques are used to reduce the dimensionality of the feature space by removing irrelevant or redundant features from the original set. This article presents a hybrid feature selection method that is based on the Butterfly optimization algorithm (BOA) and the Ant Lion optimizer (ALO) to form a hybrid BOAALO method.

Radiologist-Level Two Novel and Robust Automated Computer-Aided Prediction Models for Early Detection of COVID-19 Infection from Chest X-ray Images.

COVID-19 is an ongoing pandemic that is widely spreading daily and reaches a significant community spread. X-ray images, computed tomography (CT) images and test kits (RT-PCR) are three easily available options for predicting this infection. Compared to the screening of COVID-19 infection from X-ray and CT images, the test kits(RT-PCR) available to diagnose COVID-19 face problems such as high analytical time, high false negative outcomes, poor sensitivity and specificity.

An enhanced deep image model for glaucoma diagnosis using feature-based detection in retinal fundus.

This paper proposes a deep image analysis-based model for glaucoma diagnosis that uses several features to detect the formation of glaucoma in retinal fundus. These features are combined with most extracted parameters like inferior, superior, nasal, and temporal region area, and cup-to-disc ratio that overall forms a deep image analysis. This proposed model is exercised to investigate the various aspects related to the prediction of glaucoma in retinal fundus images that help the ophthalmologist in making better decisions for the human eye.