MSRNet: Multiclass Skin Lesion Recognition Using Additional Residual Block Based Fine-Tuned Deep Models Information Fusion and Best Feature Selection
Open accessAbstract
Cancer is one of the leading significant causes of illness and chronic disease worldwide. Skin cancer, particularly melanoma, is becoming a severe health problem due to its rising prevalence. The considerable death rate linked with melanoma requires early detection to receive immediate and successful treatment. Lesion detection and classification are more challenging due to many forms of artifacts such as hairs, noise, and irregularity of lesion shape, color, irrelevant features, and textures. In this work, we proposed a deep-learning architecture for classifying multiclass skin cancer and melanoma detection. The proposed architecture consists of four core steps: image preprocessing, feature extraction and fusion, feature selection, and classification. A novel contrast enhancement technique is proposed based on the image luminance information. After that, two pre-trained deep models, DarkNet-53 and DensNet-201, are modified in terms of a residual block at the end and trained through transfer learning. In the learning process, the Genetic algorithm is applied to select hyperparameters. The resultant features are fused using a two-step approach named serial-harmonic mean. This step increases the accuracy of the correct classification, but some irrelevant information is also observed. Therefore, an algorithm is developed to select the best features called marine predator optimization (MPA) controlled Reyni Entropy. The selected features are finally classified using machine learning classifiers for the final classification. Two datasets, ISIC2018 and ISIC2019, have been selected for the experimental process. On these datasets, the obtained maximum accuracy of 85.4% and 98.80%, respectively. To prove the effectiveness of the proposed methods, a detailed comparison is conducted with several recent techniques and shows the proposed framework outperforms.
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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 [42]
The Conclusions (section 5; page unavailable) report improved skin-lesion classification using fused features and say the framework can aid lesion localization with GradCAM. The paper describes dermoscopic images, not macro-scale images, and does not show that fusion itself improves localization. (LLM, conf=0.86)
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Cites
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Melanoma Cancer Classification Using ResNet with Data Augmentation
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“In this work, a metaheuristic algorithm is implemented named the Marine Predator Algorithm (MPA) [48] and modified further with an entropy technique called Reyni Entropy.”
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“The analysis of medical images of different organs of the body to detect irregular behavior plays a vital role in the medical field, such as skin cancer [1], brain cancer [2], lung cancer [3], breast cancer [4], and retina [5].”
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“It is one of the most common forms of cancer in humans because it is the body’s largest organ [7].”
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Method
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“For instance, Bibi et al. [42]. proposed MSRNet, which uses fine-tuned deep model information fusion based on additional residual blocks and optimal feature selection for multi-class skin lesion identification, achieving 85.4% and 98.8% accuracy on ISIC2018 and ISIC2019 datasets, respectively.”
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“This study [54] proposes a highly sophisticated neural network model for the early detection and classification of different skin cancers, including melanoma, an aspect that should be addressed considering their high mortality rates and increased prevalence among the population.”
A Personalized Multimodal Federated Learning Framework for Skin Cancer Diagnosis
Fan, S., Ahmed, A., Zeng, X., Xi, R., & Hou, M. (2025) · Electronics · doi:10.3390/electronics14142880 · Cited by 13 · see citing works
Robustly detecting mpox and non-mpox using a deep learning framework based on image inpainting
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SkinFLNet: A Federated Learning Approach for Skin Cancer Detection Utilizing Skin Dermoscopy Images
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“Despite this, diagnosing SC can be difficult [6], and visual skin examinations by dermatologists can take a lot of time and may vary depending on individual judgment [7].”
BADM: Boundary-Assisted Diffusion Model for Skin Lesion Segmentation
Huang, Z., Li, J., Mao, N., & Li, J. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.109213 · Cited by 11 · see citing works
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“A CNN trained on dermoscopic images would achieve excellent performance in identifying psoriasis, which might improve the management of psoriasis in areas with limited medical resources (Bibi et al. 2023 ; Dill - shad et al. 2023 ).”
Khan, M. A., Hamza, A., Shabaz, M., Kadry, S., Rubab, S., Bilal, M. A., Akbar, M., & Manic, K. S. (2024) · Discover Applied Sciences · doi:10.1007/s42452-024-05998-9 · Cited by 8 · see citing works
“Skin cancer is a widespread, deadliest, and considered a most common type of cancer, which has increased remarkably in the last few years [1].”
InfluentialSelf-citationRetractedMultimodal deep learning ensemble framework for skin cancer detection
Saeed, M. A., Afify, Y. M., Badr, N. L., & Helal, N. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-30534-z · Cited by 7 · see citing works
TFCNet: A texture-aware and fine-grained feature compensated polyp detection network
Pan, X., Mu, Y., Ma, C., & He, Q. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108144 · Cited by 7 · see citing works
Yang, J., Wan, Y., Diao, S., Alfarraj, O., Alblehai, F., Tolba, A. K. R., Shaikh, Z. A., Por, L. Y., Alizadehsani, R., & Zhang, Y. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.108668 · Cited by 8 · see citing works
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.”
Automatic Concrete Surface Crack Recognition Using EfficientNetV2 Variants
Rama, A., Damaševičius, R., Arunmozhi, S., Mohammed, M. A., Hussam, R., & Rajinikanth, V. (2023) · International Conference on System, Computation, Automation and Networking (ICSCAN) · doi:10.1109/icscan58655.2023.10395683 · Cited by 6 · see citing works
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“…imaging and diagnosis [35,37,38,40,41,44,88], cardiovascular disease prediction and management [47,49,69], dermatological lesions recognition [64,65], or infection detection [53,101]Moreover, in his prior studies, Saab et al. described the use of ML for general therapeutic purposes such as the…”
Mertahi, A. E., Ezzine, H., Douzi, S., & Douzi, K. (2025) · PLoS ONE · doi:10.1371/journal.pone.0328402 · Cited by 6 · see citing works
“In 2023, Bibi et al [23] developed a system called MSRNet to assist in the classification of skin lesions using deep learning techniques.”
InfluentialTransfer Learning Empowered Skin Diseases Detection in Children
Alnuaimi, M. N., Alqahtani, N., Gollapalli, M. A. S., Rahman, A., Alahmadi, A. Y., Bakry, A., Youldash, M. M., Alkhulaifi, D., Ahmed, R., & Al-Musallam, H. A. (2024) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2024.055303 · Cited by 6 · see citing works
Explainable Cubic Attention-Based Autoencoder for Skin Cancer Classification
Nasir, I. M., Tehsin, S., Damaševičius, R., Zielonka, A., & Woźniak, M. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-81596-6_11 · Cited by 6 · see citing works
Self-citationA Bibliometric Review of Deep Learning Approaches in Skin Cancer Research
Supriyanto, C., Salam, A., Zeniarja, J., Utomo, D. W., Dewi, I. N., Paramita, C., Wijaya, A., & Safar, N. Z. M. (2025) · Computation · doi:10.3390/computation13030078 · Cited by 9 · see citing works
Kim, D. Y., Kim, H. K., Lim, Y. J., & Sunwoo, M. H. (2024) · IEEE Access · doi:10.1109/access.2024.3396153 · Cited by 6 · see citing works
“In brain imaging cases, computed tomography (CT) and magnetic resonance imaging (MRI) are the most widely used methods and provide support for clinical AD diagnosis by identifying cerebral structure patterns [13] and [14].”
MethodBabu, S. A., Ranganath, A., Goswami, M. M., Gnanaprakasam, T., & Ishak, M. K. (2024) · IEEE Access · doi:10.1109/access.2024.3386570 · Cited by 5 · see citing works
“MPA is a metaheuristic algorithm and is further modified with an entropy algorithm named Reyni Entropy [21].”
MethodDillshad, V., Khan, M. A., Nazir, M., Ahmad, J. E., AlHammadi, D. A., Houda, T., Cho, H.-C., & Chang, B. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.063851 · Cited by 5 · see citing works
“In [35], authors presented a novel contrast enhancement technique and then used modified DarkNet-53 and DenseNet-201 for transfer learning.”
Self-citationAdvancing skin cancer diagnosis with deep learning and attention mechanisms
Yao, Y., Lilhore, U. K., Simaiya, S., Aldossary, S. M., Tekeste, L. G., Ghith, E. S., & Abdallah, H. A. (2025) · Scientific Reports · doi:10.1038/s41598-025-33597-0 · Cited by 5 · see citing works
“Doing normalization will help speed up the training process of the model, as well as keep the pixel values on a consistent scale, thus making our model less sensitive to variations in lighting or contrast between images 26 .”
Singh, C., Ranade, S. K., & Singh, S. P. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106512 · Cited by 5 · see citing works
Polar contrast attention and skip cross-channel aggregation for efficient learning in U-Net
Lawal, M., & Yi, D. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.109047 · Cited by 5 · see citing works
Fan, C., Zhu, Z., Peng, B., Xuan, Z., & Zhu, X. (2024) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-024-01223-6 · Cited by 5 · see citing works
“To screen and diagnose skin diseases, dermatologists perform visual inspection using dermoscopic images to analyze skin lesions and their surrounding tissues [3–5].”
Bhuiyan, J. A., Efat, A. H., Hasan, M. S., & Mansia, F. M. (2026) · Digital Health · doi:10.1177/20552076261433750 · Cited by 4 · see citing works
AutoRhythmAI: A Hybrid Machine and Deep Learning Approach for Automated Diagnosis of Arrhythmias
Jayanthi, S., & Devi, S. P. (2024) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2024.045975 · Cited by 4 · see citing works
Roy, A. L., Maruf, A. A., Sony, R. I., Roy, A. P., Prova, N. N. I., & Aung, Z. (2025) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-025-04984-2 · Cited by 4 · see citing works
“Bibi et al. ( 2023), the authors proposed a deep-learning framework for detecting both Melanoma and multi-class skin cancers.”
Federated learning for privacy-preserving skin cancer classification using deep neural networks
Alfalahi, M. A. M., Karan, O., Kurnaz, S., & Türkben, A. K. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1720748 · Cited by 3 · see citing works
“Historically, clinical diagnosis has predominantly relied on expert dermatological assessments, which are limited by the subjective nature of visual perception, inter-observer variability, and restricted availability in disadvantaged areas (6, 7).”
Yang, G., Geng, Z., & Wang, Q. (2024) · IEEE Access · doi:10.1109/access.2024.3446808 · Cited by 3 · see citing works
“In this experiment, the dataset was expanded by contrast enhancement [43] and random rotation with angle − 25 ◦ ∼ 25 .”
MethodDeep Neural Networks for Skin Cancer Classification: Analysis of Melanoma Cancer Data
Afrifa, S., Varadarajan, V., Appiahene, P., Zhang, T., Gyamfi, D., & Gyening, R.-M. O. M. (2025) · Journal of Advances in Information Technology · doi:10.12720/jait.16.1.1-11 · Cited by 3 · see citing works
“Melanoma can spread to other regions of the body and is caused by malignant transformation of melanocytes produced from neural crest neoplasia [4].”
VSGNet: visual saliency guided network for skin lesion segmentation
Cai, Z., Fan, Y., Fang, T., & Wu, W. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.128635 · Cited by 3 · see citing works
BioSkinNet: A Bio-Inspired Feature-Selection Framework for Skin Lesion Classification
Akram, T., Almarshad, F. A., Alsuhaibani, A., & Naqvi, S. R. (2025) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2025.064079 · Cited by 2 · see citing works
Towards Explainable Skin Cancer Diagnosis: A Vision Transformer Approach with Grad-CAM Visualization
Bouzidi, S., Jdey, I., & Drira, F. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227715 · Cited by 2 · see citing works
Parhi, R., Jena, G. K., Garg, A., Dhobi, M., & Mondal, S. (2026) · Journal of Holistic Integrative Pharmacy · doi:10.1016/j.jhip.2026.03.002 · Cited by 2 · see citing works
Gomathi, R. M., Gnanavel, S., Narayana, K. E., & Dhiyanesh, B. (2024) · Automatika · doi:10.1080/00051144.2024.2396167 · Cited by 2 · see citing works
“A study, focussing on using a combination of DL architectures and a new optimization algorithm to enhance the detection and classification of multiple types of skin lesions, was done in the literature [17].”
An Enhanced Approach Using AGS Network for Skin Cancer Classification
Lee, H., Cho, S., Song, J., Kim, H., & Shin, Y. (2025) · Sensors · doi:10.3390/s25020394 · Cited by 2 · see citing works
Prostate Cancer Detection using Large Vision Models on Apparent Diffusion Coefficient MRI
Pawar, A., Sontakke, M., Patil, R. N., Yadav, G., & Nalawade, A. (2025) · International Conference on Recent Advances in Information Technology · doi:10.1109/rait65068.2025.11089155 · Cited by 2 · see citing works
“[12] further implements an algorithm marine predator optimization (MPA) to select the best features extracted using DarkNet-53 and DensNet-20 and then further classify them using traditional ML algorithms like Narrow Neural Network, Weighted KNN, etc. [13][14] extract features from fine-tuned…”
Sainudeen, J. P., & Jeyaprakash, T. (2025) · International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) · doi:10.1109/access65134.2025.11135704 · Cited by 1 · see citing works
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Skin cancer detection using late fusion of pretrained models
Ksibi, A., Walha, A., Zakariah, M., Ayadi, M. F., Almujally, N. A., & Alshalali, T. A. N. (2026) · Scientific Reports · doi:10.1038/s41598-026-48687-w · Cited by 1 · see citing works
Saleh, N., Shaaban, A., & Salaheldin, A. M. (2026) · Biomedical Physics & Engineering Express · doi:10.1088/2057-1976/ae74d6 · Cited by 1 · see citing works
“Another study introduced a new contrast enhancement technique that works by processing the image’s luminance channel according to the Retinex model, thereby sharpening the distinction between the infected and healthy skin regions [40].”
Hasan, M. S., Efat, A. H., Bhuiyan, J. A., & Mansia, F. M. (2026) · Digital Health · doi:10.1177/20552076261462026 · Cited by 1 · see citing works
Dahan, F. (2024) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2024.047921 · Cited by 1 · see citing works
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Skin Lesion Diagnosis with Deep Neural Networks and Wavelet-Based Feature Analysis
Pius, S., Rahamathulla, K., & Swaraj, K. (2025) · International Conference on Advances in Computing, Communication, Embedded and Secure Systems (ACCESS) · doi:10.1109/access65134.2025.11135546 · Cited by 1 · see citing works
“A four-stage technique for classifying skin lesions was proposed by Bibi, Sobia, Khan, Muhammad Attique, and Shah[9].”
Towards Improved Skin Cancer Diagnosis: A Reliable Predictive Framework
Divya, B., & Sagar, M. (2025) · International Conference on Pervasive Computing and Social Networking (ICPCSN) · doi:10.1109/icpcsn65854.2025.11035984
“[27].”
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Al-shamasneh, A. R. M., Omer, H. K., Tawfiq, N. E., Karim, F. K., & Jalab, H. A. (2026) · Journal of King Saud University - Science · doi:10.25259/jksus_929_2025
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Skin Lesion Classification for Diverse Skin Tones Using Generative AI and YOLO
Natesan, P., Gothai, E., Manimaran, A., Neeleshwar, K., Rahul, K., & Kumar, P. R. (2026) · International Conference on Intelligent Systems for Communication, IoT and Security (ICISCoIS) · doi:10.1109/iciscois62701.2026.11447645
A Deep Learning-Based Method for the Categorization of Different Skin Diseases
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Contrast-Focused Preprocessing for Skin Lesion Segmentation and Classification
Aakaou, A., Thurnhofer‐Hemsi, K., & Domínguez, E. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-27317-8_7
Khizar, S. K., Balakrishnan, M., & Reddy, V. L. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-3-032-22114-8_16
AI-Based Resource Efficient Image Classifier for Skin Lesions
khan, S., Khan, F., Sadique, U., Khan, S., & Jan, A. (2025) · International Journal of Innovations in Science and Technology · doi:10.33411/ijist/1352
Research on Skin Disease Classification Based on Improved ResNet34
汪, 鑫. (2026) · Advances in Applied Mathematics · doi:10.12677/aam.2026.158354
Yao, B., Jin, A., Liu, H., & Li, Q. (2026) · Bioengineering · doi:10.3390/bioengineering13090989
“…in imbalanced datasets [20]; (2) integrating dermoscopic features with data-driven deep learning can enhance both accuracy and interpretability [19,21], and (3) the inherent complexity and limited interpretability of CNNs render them “black-box” models and make it difficult to understand how they…”
Dillshad, V., & Aldukhail, M. (2026) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2026.102674
Antony, C. G. M., & Baskaran, D. (2026) · Proceedings of the National Academy of Sciences India Section A Physical Sciences · doi:10.1007/s40010-026-01218-8
Uncertainty-Aware Deep Learning for Skin Cancer Diagnosis
Mohammed, A. J., & Hassan, M. M. (2026) · Dasinya Journal for Engineering and Informatics · doi:10.65542/djei.v2i4.97
“…studies like Del Amor et al. [48], Tissir et al. [51] as well as entropy-controlled feature selection models by Afza et al. [47], Bibi et al. [49], and Akram et al. [56] show entropy can inform sample filtering, feature selection, active learning, and uncertainty-driven decision-making.”
Malathi, M., & Fernandez, F. M. H. (2026) · International Conference on Intelligent Cyber Physical Systems and Internet of Things (ICoICI) · doi:10.1109/icoici69824.2026.11723384
“In the 2023 MSRNet study of Bibi et al. [14], which used feature selection and residual block fine-tuning on the HAM10000 set, an accuracy of 91.30% was obtained in seven disease classes with no explanation system, which indicates limitations in disease coverage and clinical transparency.”
Influential
- Authors
- Bibi, S., Khan, M. A., Shah, J. H., Damaševičius, R., Alasiry, A., Marzougui, M., Alhaisoni, M., & Masood, A.
- Affiliations
- COMSATS University Islamabad (PK)
- 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
- Masood, Anum
- ORCID
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2023-09-26
- Volume
- 13
- Issue
- 19
- Pages
- 3063
- ISSN
- 2075-4418 (ISSN-L)
- PMC
- PMC10572512
- OpenAlex
- W4387100990
- Cited by
- 88
- References
- 46
- Citations by year
- 2026: 19 · 2025: 33 · 2024: 34 · 2023: 2
- FWCI
- 10.69
- 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, cc-by)
- 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
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- skin lesion classification, melanoma detection, skin cancer, multi-class classification, transfer learning, Darknet-53, DenseNet201, feature fusion, feature selection, Rényi entropy, genetic algorithm hyperparameter optimization, contrast enhancement, ISIC 2018, ISIC 2019
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