Multiple disease prediction such as Diabetes, Heart disease, Kidney disease, Breast cancer, Liver disease, Malaria, and Pneumonia using supervised machine learning and deep learning algorithms.

Breast cancer has the second highest mortality rate in women next to lung cancer. As per clinical statistics, 1 in every 8 women is diagnosed with breast cancer in their lifetime. However, periodic clinical check-ups and self-tests help in early detection and thereby significantly increase the chances of survival. Invasive detection techniques cause rupture of the tumor, accelerating the spread of cancer to adjoining areas. Hence, there arises the need for a more robust, fast, accurate, and efficient non-invasive cancer detection system. Early detection can give patients more treatment options. In order to detect signs of cancer, breast tissue from biopsies is stained to enhance the nuclei and cytoplasm for microscopic examination. Then, pathologists evaluate the extent of any abnormal structural variation to determine whether there are tumors. Architectural Distortion (AD) is a very subtle contraction of the breast tissue and may represent the earliest sign of cancer. Since it is very likely to be unnoticed by radiologists, several approaches have been proposed over the years but none using deep learning techniques. AI will become a transformational force in healthcare and soon, computer vision models will be able to get a higher accuracy when researchers have the access to more medical imaging datasets. The application of machine learning models for prediction and prognosis of disease development has become an irrevocable part of cancer studies aimed at improving the subsequent therapy and management of patients. The application of machine learning models for accurate prediction of survival time in breast cancer on the basis of clinical data is the main objective. We have developed a computer vision model to detect breast cancer in histopathological images. Two classes will be used in this project: Benign and Malignant

A deep learning project aimed at early detection of breast cancer by classifying tumors as benign or malignant based on features extracted from cell images. The project demonstrates data preprocessing, model training, and evaluation using various deep learning algorithms to achieve high accuracy in predictions.

Breast Cancer Prediction using Deep Learning

This project employs an Artificial Neural Network (ANN) to predict breast cancer based on numerical features. With an accuracy of 96 % without overfitting our approach demonstrates the potential of AI in early detection

Breast cancer prediction using deep learning

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IDC prediction in breast cancer histopathology images using deep residual learning with an accuracy of 99.37% in a subset of images containing a total of 7,500 microscopic images.

A deep-learning–based time-to-event survival prediction project for breast cancer patients, using Pycox models (Nnet-survival, DeepHit, Cox-Time, NMLTR). The study analyzes 30-year survival outcomes in 3,580 patients and evaluates survival probabilities across four molecular subtypes (Luminal A, Luminal B, HER2-enriched, Triple-negative)

Early Prediction of Breast Cancer using Deep Learning based techniques

Breast Cancer Prediction using machine learning, deep learning and feature selection methods

Config files for my GitHub profile.

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Deep learning techniques for breast cancer risk prediction(CNN)

Deep learning techniques, particularly Convolutional Neural Networks (CNNs), are increasingly utilized for predicting breast cancer risk by analyzing medical imaging data. By automatically extracting intricate features from mammograms, CNNs enhance diagnostic accuracy and enable earlier detection of potential malignancies .

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Breast cancer prediction using machine learning. using python, tkinter and deep learning keras model

Breast Cancer Detection and Treatment Prediction system using machine learning and deep learning techniques.

Breast cancer classification and prediction with multi-view deep convolutional neural networks using machine learning

🫀 🎗️This repository is about using Deep Learning (ANN) for the prediction of malignant/benign Breast Cancer

A breast cancer prediction model which uses deep learning to predict wether a patient has Malignant or Benign.

🎯 Automated Breast Cancer Cell Detection using Deep Learning This capstone project leverages deep learning models to accurately classify breast cancer histopathological images. Trained on a publicly available dataset and deployed using Streamlit, the application offers a clean, user-friendly interface for real-time cancer prediction.

🩺 Breast Cancer Classification using PyTorch | 🤖 Neural Networks for Benign vs Malignant Prediction | 📈 96% Accuracy ⚡ Deep Learning with PyTorch | 🧠 Breast Cancer Tumor Detection | ✅ High Accuracy on Real-World Data 📊 Binary Classification of Breast Cancer | 🔬 Healthcare AI Project in PyTorch | 🚀 GPU-Ready 🤖 PyTorch Deep Learning Model

🩺 A deep learning based Breast Cancer Prediction App using TensorFlow, Joblib, and Streamlit. The model predicts whether a tumor is Benign or Malignant using medical diagnostic features. Interactive UI with dark theme for better user experience. Dataset: Breast Cancer Wisconsin (Diagnostic).

This repo has two ANN models: one for detecting breast cancer using the Breast Cancer dataset and another for classifying Iris flower species with the Iris dataset. Both models achieve high accuracy 💯, using deep learning techniques to make predictions and classifications with ease.

AI-Based Disease Detection & Guidance System is a healthcare application that predicts diseases like heart disease, diabetes, breast cancer, and brain tumors using machine learning and deep learning. It provides real-time predictions, MRI analysis with explainability, and personalized health recommendations.

Breast Cancer Prediction using PyTorch: Built a feedforward neural network on the Wisconsin dataset to classify tumors as benign or malignant. Implemented preprocessing, a 1-hidden-layer (64 neurons, ReLU) model, trained with Adam & BCE loss, and achieved strong accuracy—showcasing deep learning in healthcare.

"Breast cancer prediction powered by Machine Learning . This project explores real-world medical data, performs deep analysis, and compares multiple models to identify whether tumors are malignant or benign. Achieved high accuracy using advanced ML techniques, data visualization, and evaluation metrics. A step forward in applying AI for healthcare

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