This study presents a comprehensive review of the potential of multimodal deep learning
(DL) in medical diagnosis, using COVID-19 as a case example. Motivated by the success of …

This study presents a novel amalgamated model for the diagnosis of multiple medical
conditions using various imaging modalities, including Chest X-ray, MRI, and endoscopic …

Objective: This study explores a semi-supervised learning (SSL), pseudo-labeled strategy
using diverse datasets such as head and neck cancer (HNCa) to enhance lung cancer …

The quantity of textual data is increasing in online realm with the blink of eye and it has
become very difficult to extract useful information from such enormous bundle of information …

The current work introduces the hybrid ensemble framework for the detection and
segmentation of colorectal cancer. This framework will incorporate both supervised …

Abstract “PolynetDWTCADx” is a sophisticated hybrid model that was developed to identify
and distinguish colorectal cancer. In this study, the CKHK-22 dataset, comprising 24 classes …

Unrepaired DNA damage in skin cells causes mutations leading to skin cancer, a highly
aggressive malignancy. This study proposes a machine learning (ML)-based framework for …

Abstract Background and Objective Serum protein electrophoresis (SPEP) plays a critical
role in diagnosing diseases associated with M− proteins. However, its clinical application is …

Surgical waiting lists present significant challenges to healthcare systems, particularly in
resource-constrained settings where equitable prioritization and efficient resource allocation …

Effective disease management necessitates the accurate and timely prediction of lung
cancer and diabetes. Machine learning (ML) based models have garnered attention in the …