Lung cancer is among the deadliest cancers. Besides lung nodule classification and
diagnosis, develo** non-invasive systems to classify lung cancer histological …

In this paper, a feature learning enhanced convolutional neural network (FLE-CNN) is
proposed for cancer detection from histopathology images. To build a highly generalized …

Globally, patients with diabetes, diabetic retinopathy, cancer, and heart disease are growing
rapidly in developed and develo** countries. As a result of these ailments, the rate of …

Lymphoma, a heterogeneous group of blood cancers, presents significant diagnostic and
therapeutic challenges due to its complex subtypes and variable clinical outcomes. Artificial …

Abstract Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers.
Develo** non-invasive techniques for NSCLC histology characterization may not only …

This study aimed to develop a deep learning (DL) model for predicting the recurrence risk of
lung adenocarcinoma (LUAD) based on its histopathological features. Clinicopathological …

In the last few years, artificial intelligence has shown a lot of promise in the medical domain
for the diagnosis and classification of human infections. Several computerized techniques …

Background and objective: Lung cancer (LC) has a high fatality rate that continuously affects
human lives all over the world. Early detection of LC prolongs human life and helps to …

Cutaneous melanoma represents one of the most life-threatening malignancies.
Histopathological image analysis serves as a vital tool for early melanoma detection. Deep …

Large-scale labeled datasets are crucial for the success of supervised learning in medical
imaging. However, annotating histopathological images is a time-consuming and labor …