This Repository Consist of work related to the detection of Lung Cancer and Malignant Lung Nodules from Chest Radio Graphs using Computer Vision and algorithms, Image Processing and Machine Learning Technology.

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

REU research detecting lung cancer from CT scans

Lung Cancer Detection Project (Computer Vision)

Lung Cancer Detection with Resnet50, YOLOv11, GoogleNet and MobileNet

A lung cancer detection model using MRI scan images

Lung Cancer Detection using Machine Learning and Computer Vision

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Using CNN and Computer Vision, Lung Cancer Detection model is made by applying deep learning

Training a CNN model for lung cancer detection using computer vision techniques on CT scan DICOM images

AI-powered respiratory healthcare platform integrating Computer Vision, Audio Classification, and ML models for pneumonia detection, lung cancer prediction, cough analysis, and air quality risk assessment.

This project presents an end-to-end multimodal framework integrating tumor detection, TNM staging, and guideline-based treatment recommendations for lung cancer. The system unifies computer vision models with Retrieval-Augmented Generation (RAG) using modern LLMs.

Applications of deep learning in healthcare and drug discovery --Applied computer vision and deep learning by building models such as CNN (Convolutional neural network) to classify lung cancer images, YOLO object detection model in scientific literature, GNN (Graph Neural Network) to predict Blood-brain barrier permeability.