ArticleJournal of medical systems2016
Novel Approaches for Diagnosing Melanoma Skin Lesions Through Supervised and Deep Learning Algorithms.
Article in Journal of medical systems, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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Who cites it
46 citing papers in PubMed, 210 citations in OpenAlex.
- Risk factors influencing survival of acellular porcine corneal stroma in infectious keratitis: a prospective clinical study.Journal of translational medicine · 2019Trial
- Artificial Intelligence-Assisted Diagnosis of Atopic Eczema in Darker Skin Types: A CNN-Based Study.Dermatology practical & conceptual · 2026Article
- Explainable multimodal AI for skin lesion risk prediction via 3D imaging and clinical data.Scientific reports · 2025Article
- Unveiling the potential of artificial intelligence in revolutionizing disease diagnosis and prediction: a comprehensive review of machine learning and deep learning approaches.European journal of medical research · 2025Review
- Training State-of-the-Art Deep Learning Algorithms with Visible and Extended Near-Infrared Multispectral Images of Skin Lesions for the Improvement of Skin Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Advanced Deep Learning Models for Melanoma Diagnosis in Computer-Aided Skin Cancer Detection.Sensors (Basel, Switzerland) · 2025Article
- Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer.Biomedicines · 2024Review
- Skin Cancer Image Segmentation Based on Midpoint Analysis Approach.Journal of imaging informatics in medicine · 2024Article
- Shallow and deep learning classifiers in medical image analysis.European radiology experimental · 2024Review
- Development and validation of a deep learning model for improving detection of nonmelanoma skin cancers treated with Mohs micrographic surgery.JAAD international · 2024Article
- DDCNN-F: double decker convolutional neural network 'F' feature fusion as a medical image classification framework.Scientific reports · 2024Article
- Role of Artificial Intelligence in Surgical Decision-Making: A Comprehensive Review.Galen medical journal · 2024Review
- A Review of Recent Advances in Computer-Aided Detection Methods Using Hyperspectral Imaging Engineering to Detect Skin Cancer.Cancers · 2023Review
- Artificial Intelligence Algorithms for Benign vs. Malignant Dermoscopic Skin Lesion Image Classification.Bioengineering (Basel, Switzerland) · 2023Article
- Early Melanoma Detection Based on a Hybrid YOLOv5 and ResNet Technique.Diagnostics (Basel, Switzerland) · 2023Article
- Deep Learning-Based Evaluation of Ultrasound Images for Benign Skin Tumors.Sensors (Basel, Switzerland) · 2023Article
- Deep skin diseases diagnostic system with Dual-channel Image and Extracted Text.Frontiers in artificial intelligence · 2023Article
- A machine learning algorithm for early detection of heel deep tissue injuries based on a daily history of sub-epidermal moisture measurements.International wound journal · 2022Article
- Cancer-Net SCa: tailored deep neural network designs for detection of skin cancer from dermoscopy images.BMC medical imaging · 2022Article
- On the Automatic Detection and Classification of Skin Cancer Using Deep Transfer Learning.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Dermoscopy is a technique used to capture the images of skin, and these images are useful to analyze the different types of skin diseases. Malignant melanoma is a kind of skin cancer whose severity even leads to death. Earlier detection of melanoma prevents death and the clinicians can treat the patients to increase the chances of survival. Only few machine learning algorithms are developed to detect the melanoma using its features. This paper proposes a Computer Aided Diagnosis (CAD) system which equips efficient algorithms to classify and predict the melanoma. Enhancement of the images are done using Contrast Limited Adaptive Histogram Equalization technique (CLAHE) and median filter. A new segmentation algorithm called Normalized Otsu's Segmentation (NOS) is implemented to segment the affected skin lesion from the normal skin, which overcomes the problem of variable illumination. Fifteen features are derived and extracted from the segmented images are fed into the proposed classification techniques like Deep Learning based Neural Networks and Hybrid Adaboost-Support Vector Machine (SVM) algorithms. The proposed system is tested and validated with nearly 992 images (malignant & benign lesions) and it provides a high classification accuracy of 93 %. The proposed CAD system can assist the dermatologists to confirm the decision of the diagnosis and to avoid excisional biopsies.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.