This application aims to early detection of lung cancer to give patients the best chance at recovery and survival using CNN Model.

This CNN is capable of diagnosing breast cancer from an eosin stained image. This model was trained using 400 images. It has an accuracy of 80%

Lung cancer prediction applies machine learning to classify cases as normal, benign, or malignant using patient data like demographics, symptoms, or imaging. Models like Logistic Regression or CNNs are trained on labeled data and evaluated with metrics like accuracy and AUC-ROC, enabling early detection and timely treatment.

Lung Cancer Detection using CNN

Computer Vision

Metastatic Breast Cancer Detection Using CNN

This Project is a 7 Layer CNN Model consisting of 3 Convolution layers each followed by a Max Pooling Layer and Fully Connected layer on Breast Ultrasound Images to classify them as Benign, Malignant and Normal stages.

breast cancer detection using cnn (DL) & apply model at web app

Project aimed at training a deep learning model using Convolutional Neural Networks (CNN) for high-accuracy (over 99%) blood cancer detection, utilizing a large dataset of blood cell images.

Lung cancer detection using CNN. The dataset consist of normal and infected lungs images.

this website is provide lung cancer detection using cnn model

Breast cancer detection using CNN and Transfer Learning

No description available

skin cancer detection using CNN

This is an image processing project to diagnose breast cancer using histopathological image of patients. It is build with convolutional neural network (CNN) in TensorFlow, and is deployed on HuggingFace Spaces as well.

No description available

Skin cancer detection using CNNs with data analysis.

AI-powered skin cancer detection system using HybridViT (CNN + CBAM + Vision Transformer). Classifies 9 types of skin lesions with 90%+ accuracy. Professional Streamlit web app with intuitive interface.

Trained a Convolutional Neural Network to classify breast cancer as Malignant or Benign

This repository deals with the detection of lung cancer nodules in the CT scan images using deep convolutional neural network. The corresponding outputs, dataset and the documents and the google colab codes are available within the repository.

Skin Cancer detection using CNN

This project is a skin cancer detection model using Convolutional Neural Networks (CNNs) built in Python with TensorFlow and Keras. The model classifies skin cancer into benign or malignant categories using image data.

This is Lung Cancer Detection using Deep Learning

This system detects breast cancer using ultrasound images. Performance of three different architectures of CNN are compared.

Breast tumor detection using convolutional neural networks( a binary classifier of mammogram images "Normal", "Abnormal (Tumor)")

Cervical Cancer Detection using Deep learning CNN.

A deep learning project using a custom CNN architecture, ImageMaskNet, for classifying breast ultrasound images into normal, benign, and malignant categories. This work contributes to early breast cancer detection using advanced image analysis techniques.

CNN algorithm for cervical cancer detection using pap smear images under microscope.

No description available

Convolutional Neural networks can identify and classify lung cancer(histopathological dataset) types with greater accuracy in a shorter period, which is crucial for determining patients' right treatment procedure and their survival rate. Benign tissue, Adenocarcinoma, and squamous cell carcinoma are considered in this project