https://github.com/injamul3798/melanomadetector-an-ai-powered-melanoma-skin-cancer-detection-

. The proposed application, "MelanomaDetector," utilizes a Convolutional Neural Network (CNN) model, specifically a modified version of the VGG16 architecture pre-trained on the ImageNet dataset, for accurate melanoma detection
https://github.com/injamul3798/melanomadetector-an-ai-powered-melanoma-skin-cancer-detection-

cnn machinelearningprojects ml mlbased python vgg16

Last synced: 7 months ago
JSON representation

. The proposed application, "MelanomaDetector," utilizes a Convolutional Neural Network (CNN) model, specifically a modified version of the VGG16 architecture pre-trained on the ImageNet dataset, for accurate melanoma detection

• Host: GitHub
• URL: https://github.com/injamul3798/melanomadetector-an-ai-powered-melanoma-skin-cancer-detection-
• Owner: injamul3798
• Created: 2024-02-11T17:20:11.000Z (over 1 year ago)
• Default Branch: master
• Last Pushed: 2024-02-11T17:34:40.000Z (over 1 year ago)
• Last Synced: 2025-02-07T15:45:10.229Z (9 months ago)
• Topics: cnn, machinelearningprojects, ml, mlbased, python, vgg16
• Language: Jupyter Notebook
• Homepage:
• Size: 2.45 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Abstraction 

Abstract: The "MelanomaDetector" app presents a groundbreaking solution addressing the 

critical need for early detection of melanoma cancer, distinguishing between benign and 

malignant cases. This paper outlines the development and implementation of an Artificial 

Intelligence (AI) powered application, aimed at providing users with a user-friendly tool for 

monitoring skin health, facilitating early melanoma detection, and prompting timely medical 

intervention. The proposed application, "MelanomaDetector," utilizes a Convolutional Neural 

Network (CNN) model, specifically a modified version of the VGG16 architecture pre-trained on 

the ImageNet dataset, for accurate melanoma detection. The development process encompasses 

seven key phases: data collection, preprocessing, model selection, model customization, app 

development, and testing & validation. By leveraging state-of-the-art AI technology, this 

application seeks to enhance public awareness and education on melanoma, ultimately 

contributing to improved healthcare outcomes. We have achived highest accuracy 99 and 95 for 

training and testing respectively

# Dataset 

### Link : https://www.kaggle.com/datasets/fanconic/skin-cancer-malignant-vs-benign

![image](https://github.com/injamul3798/MelanomaDetector-An-AI-Powered-Melanoma-Skin-Cancer-Detection-/assets/101572467/1226dc81-86f3-4abd-9934-f2831c057b57)

# App Design or Prototype

#### Section 1

![Screenshot (576)](https://github.com/injamul3798/MelanomaDetector-An-AI-Powered-Melanoma-Skin-Cancer-Detection-/assets/101572467/b84ebe52-a77f-4b94-a248-4d8f229f89ad)

#### Section 2

![Screenshot (577)](https://github.com/injamul3798/MelanomaDetector-An-AI-Powered-Melanoma-Skin-Cancer-Detection-/assets/101572467/fc85ea96-e037-45cb-bdf4-ac9768eafc32)

# Result Visulization

![image](https://github.com/injamul3798/MelanomaDetector-An-AI-Powered-Melanoma-Skin-Cancer-Detection-/assets/101572467/fbd8f1b8-4ef3-48b5-aec1-5d13b6f160ab)

# Project Devloped by

### Md. Injamul Haque

#### ML Researcher 

#### Contact:injamulhaque9117@gmail.com