Breast Cancer Detection Using ML

Computer Engineering

Breast cancer is the second most commonly diagnosed cancer among women globally, and early detection is crucial to improving survival rates. Machine learning (ML) and artificial intelligence (AI) techniques have shown promising results in detecting breast cancer in medical imaging data. In this study, we explore the application of ML and AI algorithms for breast cancer detection using mammogram images. Breast cancer detection is a critical healthcare challenge worldwide, and Machine Learning (ML) and Artificial Intelligence (AI) are increasingly being used to aid in the detection process. ML algorithms can analyse vast amounts of data, identify patterns, and classify tumours with high accuracy. The main idea here is to utilize all the open source datasets and breast cancer detection methodologies such as K-nearest neighbour, Convolutional Neural Network, Support Vector Machines, Generative Adversarial Networks to identify pros and cons of all the methodologies. The result of this would be to find the most efficient model to work in a particular scenario. Moreover, machine learning algorithms can be trained to predict breast cancer risk and personalise screening recommendations for individual patients. As such, AI and ML have enormous potential in the fight against breast cancer, improving the diagnosis and treatment of the disease. There are several machine learning algorithms available that are used in this system including KNN, SVM, CNN, GANS, Decision Tree, Random Forest, K-means.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.