Harshit Mittal, Maharaja Agrasen Institute of Technology, India

Medical image analysis is a vital component of modern medical practice, and the accuracy of such analysis is critical for accurate diagnosis and treatment. Computed tomography (CT) scans are commonly used to visualize the kidneys and identify abnormalities such as cysts, tumors, and stones. Manual interpretation of CT images can be time-consuming and subject to human error, leading to inaccurate diagnosis and treatment. Deep learning models based on Convolutional Neural Networks (CNNs) have shown promise in improving the accuracy and speed of medical image analysis. In this study, we present a CNN-based model to accurately classify CT images of the kidney into four categories: Normal, Cyst, Tumor, and Stone, using the CT KIDNEY DATASET. The proposed CNN model achieved an accuracy of 99.84% on the test set, with a precision of 0.9964, a recall of 0.9986, and a F1-score of 0.9975 for all categories. The model was able to accurately classify all images in the test set, indicating its high accuracy in identifying abnormalities in CT images of the kidney. The results of this study demonstrate the potential of deep learning models based on CNNs in accurately classifying CT images of the kidney, which could lead to improved diagnosis and treatment outcomes for patients. This study contributes to the growing body of literature on the use of deep learning models in medical image analysis, highlighting the potential of these models in improving the accuracy and efficiency of medical diagnosis.

Medical image analysis; Computed tomography (CT); Deep learning; Convolutional Neural Networks (CNNs); CT KIDNEY DATASET;