Multimodal Deep Learning System for Advanced Skin Cancer DetectionMultimodal Deep Learning System for Advanced Skin Cancer Detection
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Project Description
Developed a multimodal deep learning system for skin cancer detection by combining dermatoscopic images with patient metadata, including age, sex, and lesion location. The model classifies seven different types of skin lesions using a hybrid architecture that merges image features with structured patient information, improving diagnostic accuracy compared to image-only approaches.

Overview
The application assists in the early classification of skin lesions by analyzing both medical images and patient information. It demonstrates how multimodal AI can improve medical image analysis and decision support.

Key Features
Multimodal Deep Learning Architecture
Classification of 7 Skin Cancer Categories
Medical Image Processing
Patient Metadata Integration
Data Preprocessing & Feature Engineering
Exploratory Data Analysis (EDA)
Model Training & Evaluation
Prediction Confidence Scores
Visual Performance Reports
Reproducible Machine Learning Pipeline

Technologies Used
Python
TensorFlow / Keras
MobileNetV2
EfficientNetB0
OpenCV
Pandas
NumPy
Scikit-learn
Matplotlib
Jupyter Notebook

My Role
Designed and implemented the complete machine learning pipeline, including:
Data preprocessing and cleaning
Exploratory Data Analysis (EDA)
Medical image preprocessing
Feature engineering for patient metadata
Multimodal neural network development
Model training and evaluation
Performance visualization
Documentation and GitHub project management

Business / Research Applications
Medical AI Research
Clinical Decision Support
Skin Lesion Classification
Healthcare AI
Medical Image Analysis
AI-Assisted Diagnosis
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Creatives on Contra have earned over $150M and we are just getting started