Early automated detection system for skin cancer diagnosis using artificial intelligent techniques

Sep 21, 2026

Skin cancer is a widespread, high-risk disease where early detection can lower mortality. Traditional diagnostic approaches such as biopsy and visual inspection are invasive or subjective, time-consuming, costly, and can contribute to delays or inconsistent results. Dermoscopy offers noninvasive imaging, while AI-based automated systems can reduce these limitations. This paper presents an early automated detection framework for dermoscopic images using adaptive snake and region growing segmentation, and compares ANN versus SVM classification.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages10

Reading Time25 min

AccessFree to download

Introduction

Skin cancer burden and need for early detection

Skin structure and cancer types

Traditional detection methods and limitations

Dermoscopy and AI-based approaches

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN Early automated detection system for skin cancer diagnosis using artificial intelligent techniques Nourelhoda M. Mahmoud1* & Ahmed M. Soliman2 Recently, skin cancer is one of the spread and dangerous cancers around the world. Early detection of skin cancer can reduce mortality. Traditional methods for skin cancer detection are painful, timeconsuming, expensive, and may cause the disease to spread out. Dermoscopy is used for noninvasive diagnosis of skin cancer. Artificial Intelligence (AI) plays a vital role in diseases’ diagnosis especially in biomedical engineering field. The automated detection systems based on AI reduce the complications in the traditional methods and can improve skin cancer’s diagnosis rate. In this paper, automated early detection system for skin cancer dermoscopic images using artificial intelligent is presented. Adaptive snake (AS) and region growing (RG) algorithms are used for automated segmentation and compared with each other. The results show that AS is accurate and efficient (accuracy = 96%) more than RG algorithm (accuracy = 90%). Artificial Neural networks (ANN) and support vector machine (SVM) algorithms are used for automated classification compared with each other. The proposed system with ANN algorithm shows high accuracy (94%), precision (96%), specificity (95.83%), sensitivity (recall)(92.30%), and F1-score (0.94). The proposed system is easy to use, time consuming, enables patients to make early detection for skin cancer and has high efficiency. Keywords Skin cancer, Artificial intelligent, Artificial neural networks, Support vector machine, Adaptive snake, Region growing In the last decades, skin cancer is considered one of the most common and spread cancers around the world. It is extremely important to detect skin cancer in the early stage to reduce mortality1,2. The skin protects the body from infection, viruses such as coronavirus3, heat and dangerous UV radiation4–6. It also can store water and fat, maintain body temperature, and form vitamin D7,8. The estimation of the World Health Organization is recorded as approximately 132,000 skin melanoma cases per year9. Middle East has the highest skin cancer rates, Egypt has 1.52 rate, and its world rank is 11710. Skin consists of two basic layers; the top layer is called epidermis which is made of flat cells called squamous cells. Basal cells are under the squamous cell, they are round cells. Among the basal cells, there are melanocytes cells which are responsible for pigment for skin color. Under the epidermis, there is dermis which contains blood cells and glands such as sweat gland8. Skin cancer is the abnormal growth of skin cells. This growth can be benign, or it can be malignant such as melanoma. Types of skin cancer are classified according to the type of cells that are subjected to cancer itself. Melanoma skin cancer, Basal skin cancer, and Squamous skin cancer are the most common types of skin cancer11, 12. Melanoma skin cancer is the most fatal and dangerous type of skin cancer. It originated from melanocytes on the skin surface13, 14. Basal skin cancer is originated from basal cells. Squamous skin cancer originated from Squamous cells15. There are many methods to detect skin cancer. Traditional methods for skin cancer detection such as BIOPSY and naked eye16, 17 (visual inspection by dermatologists or general practitioners), have several challenges and limitations. BIOPSY is an invasive detection method, it is painful way and time-consuming method, it also may cause the disease to spread out18–20. In cases where a lesion appears suspicious, BIOPSY is required for definitive diagnosis, it may cause scarring, infection, and discomfort for the patient, also it can be expensive. The naked eye is another method where doctors use their eyes and experience to determine if there is a cancer or not. Visual inspection relies heavily on the expert

FAQs

What problem does the paper address in skin cancer diagnosis?

The paper addresses the need for early skin cancer detection, noting that conventional methods are painful, time-consuming, expensive, and may lead to delayed or inconsistent diagnosis.

Which algorithms are used for automated segmentation and what comparison is made?

Adaptive snake (AS) and region growing (RG) are used for automated segmentation, and the results show AS achieves higher accuracy (96%) than RG (90%).

How is classification performed and how does the proposed system perform overall?

Artificial Neural Networks (ANN) and Support Vector Machine (SVM) are compared for classification. The system using ANN reports high accuracy and related metrics, including accuracy 94%, precision 96%, specificity 95.83%, sensitivity 92.30%, and F1-score 0.94.

Sep 21, 2026

Uploaded by Emma Wilson