Role: GenAI Engineer and System Architect

Status: Production-ready

Domain: Healthcare AI, Autonomous Agents

Role: AI Automation Engineer

Status: Production-ready

Domain: Healthcare Operations & Automation

Project Overview

Skin Lesion Segmentation is a medical image analysis project focused on accurately identifying and isolating skin lesion regions from dermoscopic images using deep learning–based semantic segmentation. The project implements and compares U-Net and ResUNet architectures to achieve precise pixel-level segmentation, a critical preprocessing step in computer-aided dermatology and early skin cancer screening systems.

Problem Statement

Manual delineation of skin lesions by clinicians is time-consuming, subjective, and prone to inter-observer variability.

Automated segmentation is challenging due to:

• → Irregular lesion boundaries
• → Low contrast between lesion and surrounding skin
• → Hair, noise, and illumination artifacts
• → Large variation in lesion size, color, and texture

The goal is to build a reliable deep learning system that accurately segments lesion regions and supports quantitative lesion analysis.

Dataset

Source: ISIC 2018 Skin Lesion Dataset

Samples: 2,596 dermoscopic image–mask pairs with expert-labeled binary segmentation masks.

Challenges Addressed: High intra-class variability, hair occlusions, fuzzy lesion boundaries.

Images were preprocessed and resized to a fixed resolution to ensure consistency during training.

System Architecture

U-Net Architecture

The baseline model uses a U-Net encoder–decoder architecture:

• → Encoder captures contextual features through progressive downsampling
• → Decoder restores spatial resolution through upsampling
• → Skip connections preserve fine-grained spatial details
• → Final 1×1 convolution produces a binary segmentation mask

ResUNet Enhancement

To improve training stability and segmentation quality, the baseline U-Net was extended to ResUNet by introducing residual connections.

Why ResUNet:

• → Improves gradient flow in deeper networks
• → Reduces vanishing gradient issues
• → Enables better learning of fine lesion boundaries
• → Stabilizes training on limited medical datasets

Training Pipeline

• → Input: 256 × 256 dermoscopic images
• → Optimizer: Adam
• → Loss Function: Dice Loss
• → Hardware: GPU-accelerated (CUDA)

Evaluation Metrics

Segmentation performance was evaluated using domain-appropriate metrics:

• → Dice Coefficient
• → Intersection over Union (IoU / Jaccard Index)
• → Precision, Recall, F1-score
• → ROC–AUC

Qualitative Results

Visual observations confirm accurate lesion boundary detection, reduced false positives, and improved handling of irregular edges. ResUNet produces smoother and more consistent masks compared to the baseline U-Net.

Quantitative Lesion Analysis

Beyond segmentation, the system performs quantitative analysis:

• → Lesion area computation
• → Width and height estimation
• → Contour extraction using predicted masks
• → Baseline vs current lesion comparison

Interactive Inference

An interactive Streamlit application allows users to upload custom dermoscopic images, perform real-time inference, and visualize segmentation masks along with lesion measurements.

Summary

Designed and implemented U-Net and ResUNet models for automated skin lesion segmentation, enhanced with residual connections for stability and accuracy. Evaluated using industry-standard metrics and deployed via an interactive Streamlit application for real-time inference and clinical analysis.