SkinNet-INIO: Multiclass Skin Lesion Localization and Classification Using Fusion-Assisted Deep Neural Networks and Improved Nature-Inspired Optimization Algorithm
Open accessAbstract
Background: Using artificial intelligence (AI) with the concept of a deep learning-based automated computer-aided diagnosis (CAD) system has shown improved performance for skin lesion classification. Although deep convolutional neural networks (DCNNs) have significantly improved many image classification tasks, it is still difficult to accurately classify skin lesions because of a lack of training data, inter-class similarity, intra-class variation, and the inability to concentrate on semantically significant lesion parts. Innovations: To address these issues, we proposed an automated deep learning and best feature selection framework for multiclass skin lesion classification in dermoscopy images. The proposed framework performs a preprocessing step at the initial step for contrast enhancement using a new technique that is based on dark channel haze and top–bottom filtering. Three pre-trained deep learning models are fine-tuned in the next step and trained using the transfer learning concept. In the fine-tuning process, we added and removed a few additional layers to lessen the parameters and later selected the hyperparameters using a genetic algorithm (GA) instead of manual assignment. The purpose of hyperparameter selection using GA is to improve the learning performance. After that, the deeper layer is selected for each network and deep features are extracted. The extracted deep features are fused using a novel serial correlation-based approach. This technique reduces the feature vector length to the serial-based approach, but there is little redundant information. We proposed an improved anti-Lion optimization algorithm for the best feature selection to address this issue. The selected features are finally classified using machine learning algorithms. Main Results: The experimental process was conducted using two publicly available datasets, ISIC2018 and ISIC2019. Employing these datasets, we obtained an accuracy of 96.1 and 99.9%, respectively. Comparison was also conducted with state-of-the-art techniques and shows the proposed framework improved accuracy. Conclusions: The proposed framework successfully enhances the contrast of the cancer region. Moreover, the selection of hyperparameters using the automated techniques improved the learning process of the proposed framework. The proposed fusion and improved version of the selection process maintains the best accuracy and shorten the computational time.
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Fusion image processing techniques can improve the localization and classification of skin lesions by analyzing macro-scale images.
“Fusion image processing techniques can improve the localization and classification of skin lesions by analyzing macro-scale images [42,43]”
From Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives (2025) · cites this paper as [43]
The Results and Discussion (§4.2) report that feature fusion improved skin-lesion classification accuracy. The Introduction (§1) identifies localization as a challenge the method aims to address, but the cited results do not establish a localization improvement. The Datasets Description (§3.1) says the images were captured by dermoscopy or dermatoscopy, not described as macro-scale images. (LLM, conf=0.87)
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Cited by
Sorted by citations
Della Mura, M., Sorino, J., Colagrande, A., Daruish, M., Ingravallo, G., Massaro, A., Cazzato, G., Lupo, C., Casatta, N., Ribatti, D., et al. (2025) · Medical Sciences · doi:10.3390/medsci13020070 · Cited by 6 · see citing works
“Fusion image processing techniques can improve the localization and classification of skin lesions by analyzing macro-scale images [42,43], suggesting their potential application for micro-and sub-millimeter-scale images.”
- Authors
- Hussain, M., Khan, M. A., Damaševičius, R., Alasiry, A., Marzougui, M., Alhaisoni, M., & Masood, A.
- Affiliations
- HITEC University (PK)
- Kaunas University of Technology (LT)
- King Khalid University (SA)
- Princess Nourah bint Abdulrahman University (SA)
- Norwegian University of Science and Technology (NO)
- Corresponding author
- Khan, Muhammad Attique
- Masood, Anum
- ORCID
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2023-09-06
- Volume
- 13
- Issue
- 18
- Pages
- 2869
- ISSN
- 2075-4418 (ISSN-L)
- PMC
- PMC10527569
- OpenAlex
- W4386476087
- Cited by
- 58
- References
- 57
- Citations by year
- 2026: 10 · 2025: 19 · 2024: 28 · 2023: 1
- FWCI
- 7.04
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diagnostics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- KTUePubl (Repository of Kaunas University of Technology) (submittedVersion, other-oa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- NORA - Norwegian Open Research Archives (submittedVersion, other-oa)
- Funders
- Deanship of Scientific Research at King Khalid University (RGP2/249/44)
- Topics
- Health Sciences › Medicine › Oncology › Cutaneous Melanoma Detection and Management
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- skin lesion classification, dermoscopic images, computer-aided diagnosis, feature fusion, feature selection, genetic algorithm hyperparameter tuning, transfer learning, contrast enhancement, deep feature extraction, ISIC 2018, ISIC 2019
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