Generate Grad-CAM Plots

skin-cancer-recognition.py

model = torch.load('skin-cancer-recognition.pth', weights_only=False, map_location=device)
threshold = 0.6

examples = [
    {'image_class': 'mel', 'image_id': 28087, 'lesion_type': 'Malignant Melanoma (mel)'},
    {'image_class': 'mel', 'image_id': 29571, 'lesion_type': 'Malignant Melanoma (mel)'},
    {'image_class': 'bcc', 'image_id': 32290, 'lesion_type': 'Basal Cell Carcinoma (bcc)'},
    {'image_class': 'vasc', 'image_id': 24867, 'lesion_type': 'Vascular Lesion (vasc)'},
    {'image_class': 'nv', 'image_id': 28507, 'lesion_type': 'Benign Melanocytic Nevi (nv)'},
]

for img in examples:
    img_path = f"{data_dir}/test/{img['image_class']}/ISIC_00{img['image_id']}.jpg"
    grad_cam(model, threshold, img['image_id'], img_path, img['lesion_type'], image_width, image_height, device)