Diagnosing diseases through medical imaging represents the visual depiction of organs and tissues that reflect the internal structure of the body for diagnostic purposes and surgical treatment. Medical imaging diagnosis is an area where deep learning is being applied with attention due to its exceptional performance. Image-based Deep Learning models are used in clinical processes, but mostly they lack transparency due to their interpretability issues. Since deep learning models are black boxes, there is a need for techniques to explain their decision-making process, thereby leading to the evolution of the Explainable Artificial Intelligence (XAI) method. We presented an experimental analysis of XAI-based medical imaging diagnosis for oesophagus cancer using Grad-CAM and LIME XAI techniques for textual and visual explanation. Furthermore, this study suggested the medical practitioner the automatic prediction and explanation of the disease density with the accuracy.