MedAL - Skin Cancer Detection System

📊 Key Results

We achieved a 97% Recall rate for Malignant cases, ensuring that the vast majority of cancerous moles are flagged for further review by a dermatologist.

Class	Precision	Recall	F1-Score	Support
Benign	0.96	0.64	0.77	360
Malignant	0.69	0.97	0.81	300
Overall Accuracy			79%	660

🧠 Model Architectures

We experimented with two distinct architectures to benchmark performance:

1. Transfer Learning (EfficientNetB0)

Base: Pre-trained on ImageNet (frozen weights).
Head: Global Average Pooling + Dropout (0.3) + Dense Output.
Why: EfficientNet provides excellent feature extraction with lower computational cost than ResNet.

2. Custom CNN (Built from scratch)

Structure: 3 Blocks of Conv2D + MaxPooling.
Activation: Used GELU (Gaussian Error Linear Units) instead of ReLU for smoother gradient flow.
Regularization: Heavy Dropout (0.5) to prevent overfitting on the medical dataset.

⚙️ Engineering Pipeline

Data Preprocessing & Augmentation

Medical datasets are often imbalanced or limited. We implemented a robust tf.data pipeline:

Resizing: Standardized to (224, 224).
Augmentation Layer: Random Flips, Rotations (10%), Zooms (10%), and Contrast adjustments.
Normalization: Rescaling pixel values to [0,1].

Optimization Strategy

Loss Function: BinaryCrossentropy.
Optimizer: Adam.
Callback: EarlyStopping (patience=3) to prevent overfitting and restore best weights.

The “Recall-First” Approach

Standard classification uses a decision threshold of 0.5. However, for cancer detection, this is often too conservative. We analyzed the Precision-Recall Curve and adjusted the decision threshold to 0.37.

Result: This shift sacrificed some Precision (more false alarms) to significantly boost Recall (catching more cancer), which is the medically correct trade-off.

🛠️ Tech Stack

Core: Python, TensorFlow, Keras.
Data Viz: Matplotlib, Seaborn (Confusion Matrices).
Metrics: Scikit-Learn (classification_report, precision_recall_curve).

🚀 Usage

# Load the model
model = tf.keras.models.load_model('medal_model.h5')

# Predict on an image
img = load_and_prep_image('mole.jpg')
prob = model.predict(img)

# Apply the medical threshold
is_malignant = prob > 0.37