Application for Breast Cancer Detection using MATLAB

Breast cancer detection represents a critical task in medical diagnostics, where early and accurate diagnosis significantly improves patient survival rates. MATLAB serves as a powerful scientific computing platform that, when combined with its extensive machine learning and image processing toolboxes, can effectively assist in breast cancer diagnosis.

### Application Overview MATLAB-based breast cancer detection applications typically process datasets containing 200+ patient cases. These datasets are commonly stored in `.names` and `.data` file formats, containing clinical features and diagnostic outcomes. Using machine learning algorithms, the program analyzes this data to predict tumor malignancy, thereby supporting physicians in making more accurate diagnostic decisions. The implementation could involve using MATLAB's `readtable()` function to import data files and creating predictive models using the Classification Learner app or programming interfaces.

### Core Functionality Data Preprocessing: MATLAB efficiently loads and cleans data from `.data` files using functions like `rmmissing()` to handle missing values and `filloutliers()` for anomaly detection, ensuring data integrity for subsequent analysis. Feature Selection: The application employs statistical methods or machine learning techniques such as Principal Component Analysis (PCA) using `pca()` function or mutual information criteria to identify the most discriminative features, enhancing classifier performance. Classification Algorithms: Common algorithms including Support Vector Machines (SVM) implemented via `fitcsvm()`, decision trees using `fitctree()`, and neural networks through `patternnet()` provide ready-to-use implementations for rapid model development and optimization. Performance Evaluation: The system utilizes confusion matrices generated by `confusionmat()` and ROC curves created with `perfcurve()` to assess model accuracy, sensitivity, and specificity, ensuring clinical applicability.

### Extension Possibilities Beyond traditional machine learning approaches, the application can explore deep learning implementations for breast cancer detection, such as using Convolutional Neural Networks (CNN) with `imageDatastore()` and `trainNetwork()` functions to process mammography images for enhanced detection accuracy. Additionally, incorporating explainable AI techniques using LIME (Local Interpretable Model-agnostic Explanations) or SHAP values can help physicians understand model decision rationale, thereby increasing clinical trustworthiness.