ML-based Breast Ultrasound Classification for Early Cancer Detection

Breast cancer diagnosis accuracy is critical for early intervention and improved patient outcomes. Our project presents an ML model for classifying breast ultrasounds into normal, benign, and malignant categories, streamlining tumor detection and enhancing diagnostic efficiency. Targeting healthcare professionals, medical imaging researchers, and breast cancer patients, our model provides a reliable second opinion. The model also reduces human error, and aids in identifying subtle patterns in ultrasound images. Integration with information systems facilitates image analysis for medical professionals and researchers. By enabling accurate and timely detection of early-stage breast cancer, our project contributes to more effective treatments and better patient outcomes.

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Description

Project Description

Our project focuses on the development and implementation of an ML model that classifies breast ultrasound images into three categories: normal, benign, and malignant. The model is trained on a diverse dataset of ultrasound images, ensuring high accuracy and generalisability across various demographics and clinical settings.

Project key objectives

Developing an ML model that can accurately and efficiently classify breast ultrasound images.
Validating the model's performance against a test dataset and through comparison with expert radiologists' interpretations.
Creating a user-friendly interface that allows healthcare professionals and researchers to upload and analyze breast ultrasound images seamlessly. This provides a reliable second opinion and reduce human error in breast cancer diagnosis.

The primary beneficiaries of our project include radiologists, oncologists, medical imaging researchers, and breast cancer patients. By providing a reliable and efficient tool for early detection of breast cancer, our project aims to contribute to more effective treatment strategies and improved patient outcomes. Furthermore, our ML model can help medical researchers better understand breast cancer's growth patterns, leading to advancements in diagnostic methods and therapeutic interventions.