Patient Disease Detection and Abnormality Prediction using x-ray images

Authors: Mrs. J. N. Vadje, Shubham Ransure, Atharv Pitrubhakta, Gaurav More, Arshaan Shaikh

Country: India

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Abstract: Automatic defects detection in CT images is very important in many diagnostic and therapeutic applications. Because of high quantity data in CT images and blurred boundaries, tumor segmentation and classification is very hard. This work has introduced one automatic lung cancer detection method to increase the accuracy and yield and decrease the diagnosis time. The goal is classifying the tissues to three classes of normal, benign and malignant. In MR images, the amount of data is too much for manual interpretation and analysis. During past few years, lung cancer detection in CT has become an emergent research area in the field of medical imaging system. Accurate detection of size and location of lung cancer plays a vital role in the diagnosis of lung cancer. The diagnosis method consists of four stages, pre-processing of CT images, feature, extraction, and classification, the features are extracted based on DTCWT and PNN. In the last stage, PNN employed to classify the Normal and abnormal.

Keywords: Deep Learning, OpenCV, lung cancer, Dual-Tree Complex wavelet transformation

Paper Id: 230626

Published On: 2024-05-03

Published In: Volume 12, Issue 3, May-June 2024

Cite This: Patient Disease Detection and Abnormality Prediction using x-ray images - Mrs. J. N. Vadje, Shubham Ransure, Atharv Pitrubhakta, Gaurav More, Arshaan Shaikh - IJIRMPS Volume 12, Issue 3, May-June 2024.

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