Melanoma Cancer Diagnosis | Image Processing

Project Overview #

Custom image processing algorithm for melanoma cancer detection and diagnosis, built from scratch without external libraries.

Key Features #

• Feature Extraction: Custom algorithms to identify melanoma characteristics in medical images
• Image Comparison: Comparative analysis system for diagnostic accuracy
• From-Scratch Development: Built entire image processing pipeline without external libraries
• Medical Focus: Specialized in dermatological image analysis for clinical support

Technologies Used #

• Python: Core programming language
• NumPy: Numerical computations and array processing
• Custom Algorithms: Proprietary feature extraction and comparison methods
• Preprocessing: Image enhancement and noise reduction
• Segmentation: Lesion boundary detection and isolation
• Feature Analysis: Color, texture, and morphological feature extraction
• Classification: Pattern recognition for malignant vs. benign classification

Medical Applications #

• Early Detection: Support for early-stage melanoma identification
• Clinical Support: Decision support tool for medical professionals
• Screening Programs: Potential integration into cancer screening workflows
• Research Tool: Platform for further medical image analysis research

Technical Skills Demonstrated #

• Image Processing: Advanced digital image manipulation and analysis
• Algorithm Design: Custom algorithm development for specific medical needs
• Mathematical Modeling: Applied mathematical concepts to medical diagnosis
• Python Programming: Efficient implementation using core Python libraries

Impact and Significance #

• Medical Innovation: Contributed to computer-aided medical diagnosis
• Research Foundation: Established base for future medical imaging projects
• Technical Excellence: Demonstrated ability to create complex algorithms from scratch
• Healthcare Application: Direct application to real-world medical challenges