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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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Related Experiment Video

Updated: Jul 29, 2025

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Combining Reflectance Confocal Microscopy with Optical Coherence Tomography for Noninvasive Diagnosis of Skin Cancers via Image Acquisition

Published on: August 18, 2022

Diagnosing Melanomas in Dermoscopy Images Using Deep Learning.

Ghadah Alwakid¹, Walaa Gouda², Mamoona Humayun³

¹Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakakah 72341, Saudi Arabia.

Diagnostics (Basel, Switzerland)

|May 27, 2023

Summary

This summary is machine-generated.

This study enhances melanoma diagnosis using deep learning models like Inception-V3 and InceptionResnet-V2. The AI models achieved high accuracy, improving early skin cancer detection and patient outcomes.

Keywords:

Inception-V3 InceptionResnet-V2 artificial intelligence deep learning diagnostics melanoma

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

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Area of Science:

Dermatology
Computer Science
Medical Imaging

Background:

Melanoma is a prevalent and deadly skin cancer.
Early detection significantly improves patient survival rates.
Artificial intelligence (AI) shows promise in improving healthcare, including diagnostic accuracy.

Purpose of the Study:

To evaluate the effectiveness of deep learning models for melanoma recognition.
To fine-tune Inception-V3 and InceptionResnet-V2 models for improved skin cancer diagnosis.

Main Methods:

Utilized the HAM10000 dataset containing seven types of skin cancer.
Employed data augmentation techniques to address dataset imbalance.
Trained newly added top layers and fine-tuned frozen feature extraction layers of Inception-V3 and InceptionResnet-V2 models.

Main Results:

Inception-V3 model achieved an accuracy of 0.89.
InceptionResnet-V2 model achieved an accuracy of 0.91.
The proposed models demonstrated superior performance compared to previous investigations.

Conclusions:

Deep learning models, specifically Inception-V3 and InceptionResnet-V2, are effective tools for melanoma diagnosis.
AI-driven approaches can enhance the accuracy and efficiency of skin cancer detection.
Further research in AI for medical imaging can lead to improved patient outcomes.