Classification of Breast Cancer diagnosis Using Support Vector Machines

This project uses mammograms for breast cancer detection using deep learning techniques.

Machine Learning algorithms that predict whether a breast cancer tumor is Malignant or Benign

Classifying Breast Cancer Molecular Subtypes

Classification of Breast Lesion contours to Benign and Malignant Categories.

This project uses mammograms for breast cancer detection using deep learning techniques.

Breast cancer diagnoses with four different machine learning classifiers (SVM, LR, KNN, and EC) by utilizing data exploratory techniques (DET) at Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD).

Medical Image processing project.

Breast cancer is one of the most common causes of death among women worldwide. Early detection helps reduce the number of premature deaths. In the study, I am working on creating a convolutional neural network capable of identifying tumor areas within medical images (which were taken with ultrasound).

It Predicts whether the given patient is having a Malignant or Benign tumor based on the attributes in the given dataset.

Determination of whether a tumor is malignant or benign. Accuracy is 97.37%

Built a convolutional neural network to classify the malignant or benign tumor and its severity.

Worked with Dr. Shandong Wu at University of Pittsburgh to use software to improve outcomes for breast cancer risk detection.

Performing Artificial Neural Networks (ANN) to classify malignant and benign tumors in breast cancer patients.