Reference no: EM133839697

Question: Implement the given reserarch paper

Objective
The goal of this project is to implement the methodology from the paper "Fusing Metadata and Dermoscopy Images for Skin Disease Diagnosis" using the dataset. The model should integrate dermoscopy images with metadata (age, gender, lesion location) using a multiplication-based fusion strategy to improve classification accuracy, particularly for rare skin diseases.

Step-by-Step Implementation Plan

Download the dataset:

The HAM10000 dataset can be found here (on Kaggle).

It consists of 10,015 dermoscopic images and corresponding metadata (age, gender, and lesion location).
Preprocess the dataset:
Convert metadata into a structured format (CSV or Pandas DataFrame).
Handle missing metadata values:
Age ? Fill with mean age.
Gender ? Most common gender in dataset.
Location ? Most frequent lesion location.
Convert categorical metadata (gender, location) to one-hot encoding.
2?? Data Preprocessing & Augmentation

Image Processing:

Resize images to 300×300 for CNN models like ResNet, DenseNet, SENet, and PNASNet.
Apply data augmentation (rotation, brightness changes, flipping, and contrast adjustment).
Normalize pixel values (scaling between 0 and 1).
Metadata Representation:

Convert age to a normalized numerical value (0 to 1).
Convert gender & lesion location into one-hot encoded vectors.
Concatenate the metadata into a feature vector.
3?? Model Architecture: Multiplication-Based Fusion
?? Baseline CNN Classifier (Without Metadata)
Implement a standard CNN model (ResNet50, DenseNet161, or PNASNet-5).
Train the model using only dermoscopic images.
Measure classification performance (accuracy, recall, F1-score).
?? Fusion-Based Model (Using Metadata)
Instead of simply concatenating metadata to CNN features, use the multiplication-based fusion method:
Extract image features from the final convolutional layer of CNN.
Pass metadata through a two-layer fully connected (FC) network:
Layer 1: FC + ReLU activation.
Layer 2: FC + Sigmoid activation.
Multiply metadata features with CNN feature maps.
Train a classifier on these fused features.
4?? Model Training
Loss Function: Use weighted cross-entropy to handle class imbalance.
Optimizer: Stochastic Gradient Descent (SGD) with momentum (0.9). Get reliable assignment help from professional tutors!
Learning Rate Schedule: Start with 0.001, decay by 10 at epochs 50, 120, and 200.
Training Strategy:
Use 5-fold cross-validation to evaluate performance.
Train for 250 epochs.
5?? Evaluation & Comparison
Compare Models:
CNN without metadata (baseline).
CNN with concatenation-based fusion.
CNN with multiplication-based fusion (proposed approach).
Metrics to Measure:
Mean class recall (MCR)
Overall accuracy
Confusion matrix
Per-class recall for rare diseases
Heatmap Analysis:
Use Class Activation Maps (CAMs) to visualize model focus.
?? Data Augmentation Techniques Used in the Paper
The following augmentations were applied to dermoscopy images:

Random Cropping

Extract patches of size 224×224 (for ResNet, VGG, DenseNet, and SENet).
For PNASNet, patches of 331×331 were used.
Random Horizontal & Vertical Flipping

Flips images randomly to simulate variations in lesion appearance.
Random Brightness, Contrast, and Saturation Adjustments

Brightness Variation: Helps in cases where lighting conditions vary.
Contrast & Saturation Changes: Ensures model robustness against skin tone variations.
Random Rotation

Rotates images within a certain range (e.g., ±30°) to generalize the model.

The tutor should provide: ? Python code (Jupyter Notebook/PyTorch).
- Trained model weights for different architectures.
- Plots & evaluation metrics comparing models.
- Documentation on the methodology & findings.