Development of Novel Chronic Kidney Stone and Tumor Detection using Deep Sequential CNN

Kidney diseases, which include kidney stones and tumors, are caused by dietary habits, dehydration, genetic factors, and environmental conditions. Traditional diagnostic methods, such as CT scans and MRIs, are time-consuming, prone to human error, and rely on the interpretation of radiologists, leading to delays or misdiagnosis. Therefore, we will propose a CNN-based deep learning model that will automate the detection of kidney diseases from CT scan images, which improves accuracy and efficiency. This will make it easier for early diagnosis by the model into normal, cystic, tumor, or presence of stones within the gallstones. Furthermore, a web-based interface is developed, through which users can upload their CT images to obtain the predictions in real time. A diagnostic approach based on the improved model can bring accuracy to diagnostic procedures and significantly speed up analysis without reliance on manual interpretation. This system improves the patient outcomes and accelerates more prompt access to medical aid in resource poor environments.