PCMamba: Integrating partial convolution and state space models for lung cancer histopathological image classification

Data-efficient image transformer (DeiT) for high accuracy lung cancer histopathology classification

Multi-class lung cancer detection and classification from CT scans using CNN transformer

Automatic lung cancer classification using histopathological images: A hybrid DenseNet121-slantlet transform framework

Accurate classification of histopathological images is essential in the early diagnosis and treatment of lung cancer. Conventional deep learning approaches often face the challenges of simulating fine-grained texture features of low-level features as well as high-level semantic features that can be observed in complex...

ENHANCING SPATIAL-TEMPORAL FEATURE FUSION WITH CONVOLUTIONAL GRUS FOR BREAST TUMOR CLASSIFICATION

Even today, one of the main reasons for mortality among females is breast cancer, whose effective treatment needs earlier detection. In this work, a new methodology based on spatial-temporal Deep Learning for detecting and classifying breast tumors through the use of Convolutional Neural Networks (CNN) and Convolutiona...

A Hybrid CNN with Feature Fusion for Patch-Level Interstitial Lung Disease Classification

The classification of Interstitial Lung Disease (ILD) patterns in Computed Tomography (CT) images is challenging due to the irregular patterns of the disease and the similarity between patterns of different tissues. Moreover, the performance of classification models can be degraded by severe class imbalance in the data...

EfficientNet -driven compound scaling for performance enhancement of CNNs in multimodal cancer imaging

This study focused on enhancing the performance of different variants of existing Convolutional Neural Network architectures by leveraging the compound scaling feature of EfficientNet.