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Skin Cancer

Skin Cancer

Skin cancer is a type of cancer that occurs when there is an abnormal growth of skin cells, usually triggered by damage to the DNA within the skin cells. It is primarily caused by exposure to ultraviolet (UV) radiation from the sun or artificial sources like tanning beds. Skin cancer is the most common type of cancer worldwide, and its incidence continues to rise.
Basal Cell Carcinoma (BCC): BCC is the most common type of skin cancer, accounting for about 80% of cases. It typically develops in...

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Updated: Aug 5, 2025

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Prediction of melanoma Breslow thickness using deep transfer learning algorithms.

Juan-Carlos Hernández-Rodríguez^1,2, Lourdes Durán-López³, Juan P Domínguez-Morales³

¹Department of Dermatology, Virgen del Rocio University Hospital, Seville, Spain.

Clinical and Experimental Dermatology

|March 27, 2023

Summary

Deep transfer learning (DTL) algorithms show promise in distinguishing in situ melanoma from invasive melanoma and predicting Breslow thickness. EfficientNetB6 demonstrated superior diagnostic accuracy, aiding dermatologists in clinical decisions.

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Area of Science:

Dermatology
Artificial Intelligence
Medical Imaging

Background:

Distinguishing in situ melanoma (MIS) from invasive melanoma is a diagnostic challenge.
Pretrained convolutional neural networks (CNNs) require further research as decision support systems.

Purpose of the Study:

Develop, validate, and compare three deep transfer learning (DTL) algorithms.
Predict MIS vs. invasive melanoma and melanoma with Breslow thickness (BT) < 0.8 mm vs. ≥ 0.8 mm.

Main Methods:

Utilized a dataset of 1315 dermoscopic images of confirmed melanomas.
Trained and evaluated ResNetV2, EfficientNetB6, and InceptionV3 algorithms.
Compared algorithm performance against 10 dermatologists using ROC curves and diagnostic accuracy metrics.

Main Results:

EfficientNetB6 achieved the highest accuracy for MIS vs. invasive melanoma (61%) and BT < 0.8 mm vs. ≥ 0.8 mm (75%).
ResNetV2 (AUC 0.76) and InceptionV3 (AUC 0.75) outperformed dermatologists (AUC 0.70) in BT comparison.
Grad-CAM maps highlighted relevant image areas used by CNNs.

Conclusions:

EfficientNetB6 showed the best prediction results, surpassing dermatologists for the 0.8 mm BT comparison.
DTL algorithms can serve as valuable ancillary tools to support dermatologists' diagnostic decisions.