ArticleJournal of healthcare engineering2022
A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification.
Article in Journal of healthcare engineering, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 11 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
11 citing papers in PubMed.
- Skin cancer segmentation and recognition from dermoscopy images: a novel framework based on improved DeepLabV3+ and network-level fused deep architectures.Journal of advanced research · 2026Article
- An advanced skin lesion segmentation and classification framework using deep learning strategies.Scientific reports · 2025Article
- Refining skin lesions classification performance using geometric features of superpixels.Scientific reports · 2023Article
- An Improved Skin Lesion Boundary Estimation for Enhanced-Intensity Images Using Hybrid Metaheuristics.Diagnostics (Basel, Switzerland) · 2023Article
- Retracted: A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification.Journal of healthcare engineering · 2023Article
- Melanoma Detection Using Deep Learning-Based Classifications.Healthcare (Basel, Switzerland) · 2022Article
- Skin Lesion Detection Using Hand-Crafted and DL-Based Features Fusion and LSTM.Diagnostics (Basel, Switzerland) · 2022Article
- A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks.Computational intelligence and neuroscience · 2022Article
- Multiresolution Mutual Assistance Network for Cardiac Magnetic Resonance Images Segmentation.Journal of healthcare engineering · 2022Article
- Analysis of Skin Cancer and Patient Healthcare Using Data Mining Techniques.Computational intelligence and neuroscience · 2022Review
- Skin Diseases Classification Using Hybrid AI Based Localization Approach.Computational intelligence and neuroscience · 2022Article
Corrections and comments
- Retraction · 2023-05-24Concerns/Issues about Data · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Skin cancer is one of the most common diseases that can be initially detected by visual observation and further with the help of dermoscopic analysis and other tests. As at an initial stage, visual observation gives the opportunity of utilizing artificial intelligence to intercept the different skin images, so several skin lesion classification methods using deep learning based on convolution neural network (CNN) and annotated skin photos exhibit improved results. In this respect, the paper presents a reliable approach for diagnosing skin cancer utilizing dermoscopy images in order to improve health care professionals' visual perception and diagnostic abilities to discriminate benign from malignant lesions. The swarm intelligence (SI) algorithms were used for skin lesion region of interest (RoI) segmentation from dermoscopy images, and the speeded-up robust features (SURF) was used for feature extraction of the RoI marked as the best segmentation result obtained using the Grasshopper Optimization Algorithm (GOA). The skin lesions are classified into two groups using CNN against three data sets, namely, ISIC-2017, ISIC-2018, and PH-2 data sets. The proposed segmentation and classification techniques' results are assessed in terms of classification accuracy, sensitivity, specificity, F-measure, precision, MCC, dice coefficient, and Jaccard index, with an average classification accuracy of 98.42 percent, precision of 97.73 percent, and MCC of 0.9704 percent. In every performance measure, our suggested strategy exceeds previous work.
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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.