Breast and Kidney Diagnosis Management System

Abstract

Worldwide, breast and kidney diseases are major health concerns that require prompt and accurate diagnostics in order to effectively treat. This project uses ultrasound images to create a web-based application that addresses the problem of disease identification. Based on accurate image analysis, the application seeks to give medical professionals a trustworthy tool for diagnosing kidney and breast cancers. This project uses a methodology that takes a multimodal approach to disease identification. The application uses machine learning algorithms to analyze ultrasound images in order to predict the presence of cancers and identify disease indicators. Furthermore, sophisticated image processing methods are applied to improve disease detection precision. To guarantee the model's performance in practical situations, a custom dataset that is tailored to the project's specific requirements is created as part of the development process. Promising results in disease diagnosis have been observed from the application's initial implementation. Based on confusion matrix analysis, the application achieves a 90% accuracy rate for breast cancer identification. Furthermore, with a score of 0.95, the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC-ROC) score demonstrates strong performance in disease prediction. These early results highlight the developed application's potential to transform cancer diagnosis procedures and enhance patient outcomes.