Automatic multiple disease prediction approach using optimized convolutional neural network layers

Quantitative chest computed tomography-based hybrid deep learning for chronic lung disease detection and staging

This research proposes a hybrid deep learning model to accurately detect chronic lung diseases in chest computed tomography (CT) scans. Current deep learning methods, such as convolutional neural networks (CNNs) and transformer models, often suffer from high computational requirements and low interpretability. This res...

Diagnosis of Kidney Disease Using Pre-Trained Convolutional Neural Network and Multilayer Perceptron

Chronic kidney disease has become an increasingly significant global health concern, highlighting the need for accurate and early diagnostic approaches. In ultrasound-based kidney disease assessment, the extraction of informative and discriminative features plays a crucial role in achieving reliable classification. In...

A Novel Hybrid Deep Learning Method for Early Detection of Lung Cancer Using Optimized Deep Neural System

A hybrid deep learning framework that pairs a 3D convolution neural networks (3D-CNN) with an recurrent neural networks (RNN) for modeling contextual dependencies across CT slices, capturing both shape-level structure of a nodule and how that structure evolves through the scan sequence.

Current Medical Imaging

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

Automated Multi-class Classification of Lung Diseases from CT-scans Using Computer Vision

Background: Computed tomography (CT) scans and X-rays play a crucial role in diagnosing lung diseases. However, challenges such as imbalanced dataset and low image contrast can deter the performance of diagnostic models. To address these issues, convolutional neural networks (CNNs) offer a promising solution for respir...