Medical Image Analysis for Detection, Treatment and Planning of Disease using Artificial Intelligence Approaches

TL;DR

This paper presents an AI-based framework for segmenting chest X-ray images to aid in disease detection and diagnosis, utilizing SegNet and Residual U-Net models with promising accuracy metrics.

Contribution

It introduces a novel segmentation framework combining data preprocessing with advanced deep learning models for improved X-ray image analysis.

Findings

SegNet achieved 0.9815 validation accuracy.

Residual U-Net achieved 0.9901 validation accuracy.

Proposed models outperform traditional methods in segmentation metrics.

Abstract

X-ray is one of the prevalent image modalities for the detection and diagnosis of the human body. X-ray provides an actual anatomical structure of an organ present with disease or absence of disease. Segmentation of disease in chest X-ray images is essential for the diagnosis and treatment. In this paper, a framework for the segmentation of X-ray images using artificial intelligence techniques has been discussed. Here data has been pre-processed and cleaned followed by segmentation using SegNet and Residual Net approaches to X-ray images. Finally, segmentation has been evaluated using well known metrics like Loss, Dice Coefficient, Jaccard Coefficient, Precision, Recall, Binary Accuracy, and Validation Accuracy. The experimental results reveal that the proposed approach performs better in all respect of well-known parameters with 16 batch size and 50 epochs. The value of validation accuracy, precision, and recall of SegNet and Residual Unet models are 0.9815, 0.9699, 0.9574, and 0.9901, 0.9864, 0.9750 respectively.