An Innovative CBR-Enhanced Approach for Skin Cancer Classification using Cascade Forest Model and Convolutional Neural Network with Attention Mechanism
DOI:
https://doi.org/10.4108/eetpht.10.3875Keywords:
skin cancer classification, case-based reasoning (CBR), retrieval phase, convolutional neural network (CNN), ccascade forest model, attention mechanism, SMOTE techniqueAbstract
INTRODUCTION: In recent years, skin cancer has emerged as a pressing concern, necessitating advanced diagnostic and classification techniques.
OBJECTIVES: This paper introduces an innovative hybrid approach that combines deep learning and machine learning to enhance the retrieval phase of the Case-Based Reasoning (CBR) system for skin cancer classification.
METHODS: The proposed approach leverages a Convolutional Neural Network (CNN) with an attention mechanism for feature extraction, which is used to build the case base. Additionally, it uses a modified cascade forest model, augmented with traditional machine learning classifiers for classification. This modified cascade forest model incorporates the XGBoost model in its initial layer to facilitate more effective ensemble learning and bolster predictive performance. Subsequently, in the following layers, it use the random forest model to capitalize on its ability to handle high-dimensional feature spaces and maintain diversity within the ensemble.
RESULTS: Rigorous experimentation on the balanced HAM10000 dermoscopic image dataset, employing the Synthetic Minority Oversampling Technique (SMOTE), demonstrates the superiority of the modified cascade forest model in multi-class skin cancer classification. This model consistently achieves the highest metrics, including accuracy (95.40%), precision (95.49%), F1-Score (95.38%), and recall (95.44%).
CONCLUSION: This research highlights the efficacy of the proposed model compared to other classifiers, emphasizing the significance of the modified cascade forest model in enhancing the accuracy and reliability of skin cancer classification.
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Alam, T. M., K. Shaukat, et al. "An Efficient Deep Learning-Based Skin Cancer Classifier for an Imbalanced Dataset." Diagnostics.(2022); 12(9): 2115. DOI: https://doi.org/10.3390/diagnostics12092115
Breiman, L. "Random forests." Machine learning.(2001); 45(1): 5-32. DOI: https://doi.org/10.1023/A:1010933404324
Chaturvedi, S. S., K. Gupta, et al. Skin lesion analyser: an efficient seven-way multi-class skin cancer classification using MobileNet. International Conference on Advanced Machine Learning Technologies and Applications, Springer.(2020). DOI: https://doi.org/10.1007/978-981-15-3383-9_15
Chaturvedi, S. S., J. V. Tembhurne, et al. "A multi-class skin Cancer classification using deep convolutional neural networks." Multimedia Tools and Applications.(2020); 79(39): 28477-28498. DOI: https://doi.org/10.1007/s11042-020-09388-2
Chawla, N. V., K. W. Bowyer, et al. "SMOTE: synthetic minority over-sampling technique." Journal of artificial intelligence research.(2002); 16: 321-357. DOI: https://doi.org/10.1613/jair.953
Cortes, C. and V. Vapnik "Support-vector networks." Machine learning.(1995); 20: 273-297. DOI: https://doi.org/10.1007/BF00994018
Gasmi, S., A. Djebbar, et al. Skin cancer prediction using deep learning models: A comparative study. Joumée Scientifique des Mathématiques et de I'Infomratique
Gasmi, S., A. Djebbar, et al. Impact of Data Augmentation Techniques on the Accuracy of InceptionV3 Model: Applied for Skin Cancer Classification. 1st International Conference on Engineering and Applied Natural Sciences.(2022).
Gasmi, S., A. Djebbar, et al. A strategy for binary skin cancer classification based on ensemble of deep learnin models. 3rd International Conference on Applied Engineering and Natural Sciences.(2022).
Gasmi, S., A. Djebbar, et al. "A Survey on Hybrid Case-Based Reasoning and Deep Learning Systems for Medical Data Classification." Handbook of Research on Foundations and Applications of Intelligent Business Analytics.(2022): 113-141. DOI: https://doi.org/10.4018/978-1-7998-9016-4.ch006
Gasmi, S., A. Djebbar, et al. "Advancing skin cancer research with machine learning and deep learning models: A systematic review." Journal of Pakistan Association of Dermatologists.(2024); 34(2): 527-546.
Jiang, L., Z. Cai, et al. Survey of improving k-nearest-neighbor for classification. Fourth international conference on fuzzy systems and knowledge discovery (FSKD 2007), IEEE.(2007). DOI: https://doi.org/10.1109/FSKD.2007.552
Keerthana, D., V. Venugopal, et al. "Hybrid convolutional neural networks with SVM classifier for classification of skin cancer." Biomedical Engineering Advances.(2023); 5: 100069. DOI: https://doi.org/10.1016/j.bea.2022.100069
Kumar, K. S., N. Suganthi, et al. "FSPBO-DQN: SeGAN based segmentation and Fractional Student Psychology Optimization enabled Deep Q Network for skin cancer detection in IoT applications." Artificial intelligence in medicine.(2022): 102299. DOI: https://doi.org/10.1016/j.artmed.2022.102299
Pham, T.-C., C.-M. Luong, et al. Deep CNN and data augmentation for skin lesion classification. Asian Conference on Intelligent Information and Database Systems, Springer.(2018). DOI: https://doi.org/10.1007/978-3-319-75420-8_54
Polat, K. and K. O. Koc "Detection of skin diseases from dermoscopy image using the combination of convolutional neural network and one-versus-all." Journal of Artificial Intelligence and Systems.(2020); 2(1): 80-97. DOI: https://doi.org/10.33969/AIS.2020.21006
Pratiwi, R. A., S. Nurmaini, et al. "Skin lesion classification based on convolutional neural networks." Computer Engineering and Applications Journal.(2019); 8(3): 203-216. DOI: https://doi.org/10.18495/comengapp.v8i3.290
Ray, S. "Disease classification within dermascopic images using features extracted by resnet50 and classification through deep forest." arXiv preprint arXiv:1807.05711.(2018).
Reddy, N. D. "Classification of dermoscopy images using deep learning." arXiv preprint arXiv:1808.01607.(2018).
Shete, A. S., A. S. Rane, et al. "DETECTION OF SKIN CANCER USING CNN ALGORITHM." INTERNATIONAL JOURNAL.(2021); 6(5).
Thurnhofer-Hemsi, K. and E. Dominguez "A convolutional neural network framework for accurate skin cancer detection." Neural Processing Letters.(2020): 1-21.
Thurnhofer-Hemsi, K. and E. Domínguez "A convolutional neural network framework for accurate skin cancer detection." Neural Processing Letters.(2021); 53(5): 3073-3093. DOI: https://doi.org/10.1007/s11063-020-10364-y
Tschandl, P., C. Rosendahl, et al. "The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions." Scientific data.(2018); 5(1): 1-9. DOI: https://doi.org/10.1038/sdata.2018.161
Vestergaard, M., P. Macaskill, et al. "Dermoscopy compared with naked eye examination for the diagnosis of primary melanoma: a meta‐analysis of studies performed in a clinical setting." British Journal of Dermatology.(2008); 159(3): 669-676. DOI: https://doi.org/10.1111/j.1365-2133.2008.08713.x
Zhang, N., Y.-X. Cai, et al. "Skin cancer diagnosis based on optimized convolutional neural network." Artificial intelligence in medicine.(2020); 102: 101756. DOI: https://doi.org/10.1016/j.artmed.2019.101756
Zhou, Z.-H. and J. Feng Deep Forest: Towards An Alternative to Deep Neural Networks. IJCAI.(2017). DOI: https://doi.org/10.24963/ijcai.2017/497
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