A two-stage preprocessing and classification approach for accurate COVID-19 detection in X-ray images

Nature·Mar 14

✦ NabkaNews BriefAuto-summarized from multiple outlets · verify with the source

Researchers are exploring various approaches to improve the accuracy of disease detection in medical images. Different methods, including deep learning and explainable AI, are being applied to detect conditions such as covid-19, liver tumors, and lung cancer from images like x-rays and ct scans. The effectiveness of these approaches for accurate classification and prediction of various diseases is being investigated.

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Leveraging deep learning and explainable AI for effective liver tumor classification from CT scan images

Frontiers·May 8

An efficient dual path deep learning framework for COVID-19 classification using lung CT scans with explainable AI

Nature·Jan 24

Accurate classification and prediction of knee osteoarthritis based on Al-Biruni Earth Radius metaheuristic optimizer and LSTM classifier

Nature·Apr 21

An optimized EfficientNetB0 framework with CLAHE-based preprocessing for accurate multi-class chest X-ray classification

Nature·Mar 28

A novel two-stage deep learning approach for lung cancer using enhanced ResNet50 segmentation and LungSwarmNet classification

Nature·Dec 6

Dual-model approach for accurate chest disease detection using GViT and swin transformer V2

Nature·Aug 28

Efficient hybrid fuzzy weighted 3D FCNN with TSO PSO optimization for accurate multi modal MRI brain tumor classification

Nature·Oct 15

Improving CNN predictive accuracy in COVID-19 health analytics

Nature·Aug 14

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