  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.
