Hybrid Model of Convolutional Neural Network and Support Vector Machine to Classify Basal Cell Carcinoma

Rojas, Jorge Alexander Ángeles; Calderon-Vilca, Hugo D.; Figueroa, Ernesto Nayer Tumi; Ramos, Kent Jhunior Cuadros; Manguinuri, Steve S. Matos; Vilca, Edwin F. Calderón

doi:10.13053/cys-25-1-3431

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…The study results indicate that both CNN and SVM models effectively distinguish between dysplastic nuclear changes within epithelial cells, showcasing their potential utility in clinical settings. The computational models, particularly the SVM and CNN demonstrated promising performance in effectively discerning between normal and abnormal nuclei, thereby presenting a potential advancement in oral cancer screening methodologies [ 10 , 11 ].…”

Section: Discussionmentioning

confidence: 99%

Automated Analysis of Nuclear Parameters in Oral Exfoliative Cytology Using Machine Learning

Mhaske,

Ramalingam,

Nair

et al. 2024

Cureus

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Background: As oral cancer remains a major worldwide health concern, sophisticated diagnostic tools are needed to aid in early diagnosis. Non-invasive methods like exfoliative cytology, albeit with the help of artificial intelligence (AI), have drawn additional interest. Aim: The study aimed to harness the power of machine learning algorithms for the automated analysis of nuclear parameters in oral exfoliative cytology. Further, the analysis of two different AI systems, namely convoluted neural networks (CNN) and support vector machine (SVM), were compared for accuracy. Methods: A comparative diagnostic study was performed in two groups of patients (n=60). The control group without evidence of lesions (n=30) and the other group with clinically suspicious oral malignancy (n=30) were evaluated. All patients underwent cytological smears using an exfoliative cytology brush, followed by routine Hematoxylin and Eosin staining. Image preprocessing, data splitting, machine learning, model development, feature extraction, and model evaluation were done. An independent t-test was run on each nuclear characteristic, and Pearson's correlation coefficient test was performed with Statistical Package for the Social Sciences (SPSS) software (IBM SPSS Statistics for Windows, Version 28.0. IBM Corp, Armonk, NY, USA). Results: The study found substantial variations between the study and control groups in nuclear size (p<0.05), nuclear shape (p<0.01), and chromatin distribution (p<0.001). The Pearson correlation coefficient of SVM was 0.6472, and CNN was 0.7790, showing that SVM had more accuracy. Conclusion: The availability of multidimensional datasets, combined with breakthroughs in high-performance computers and new deep-learning architectures, has resulted in an explosion of AI use in numerous areas of oncology research. The discerned diagnostic accuracy exhibited by the SVM and CNN models suggests prospective improvements in early detection rates, potentially improving patient outcomes and enhancing healthcare practices.

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Section: Discussionmentioning

confidence: 99%

Automated Analysis of Nuclear Parameters in Oral Exfoliative Cytology Using Machine Learning

Mhaske,

Ramalingam,

Nair

et al. 2024

Cureus

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“…In [20], Jorge Alexander Angeles Rojas proposes a model composed of four convolution blocks to perform characteristic extraction and then the classifier in the final layer where the L1-SVM loss function is implemented, resulting in a hybrid model of a convolutional neural network and a support vector machine (CNN+SVM) to classify BCC (basal cell carcinoma), with an f1-score of up to 96.2 %.…”

Section: Previous Studiesmentioning

confidence: 99%

Automated Malignant Melanoma Classification Using Convolutional Neural Networks

Guarnizo¹,

Borda²,

Poveda³

et al. 2022

Cien.Ing.Neogranadina

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This research is proposed a design of architecture for melanoma (a kind of skin cancer) recognition by using a Convolutional Neural Network (CNN), work that will be useful for researchers in future projects in areas like biomedicine, machine learning, and others related moving forward with their studies and improving this proposal. CNN is mostly used in computer vision (a branch of artificial intelligence), applied to pattern recognition in skin moles and to determine the existence of malignant melanoma, or not, with a limited dataset. The CNN classifier designed and trained in this case was built through a couple of layers of convolution and pooling stacked to form a neural network of 6 layers followed by the fully connected to complete the architecture with an output classifier. The proposed database to train our CNN is the largest publicly collection of dermoscopic images of melanomas and other skin lesions, provided by the International Skin Imaging Collaboration (ISIC), sponsored by International Society for Digital Imaging of the Skin (ISDIS), an international effort to improve melanoma diagnosis. The purpose of this research was to design a Convolutional Neural Network with a high level of accuracy to help professionals in medicine with a melanoma diagnosis, in this case, it was possible to get accuracy up to 88.75 %.

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Hybrid Convolutional Neural Network and Support Vector Machine Based on Model Optimization for Skin Cancer Classification

Fitroh,

Uyun

2023

2023 11th International Conference on Cyber and IT Service Management (CITSM)

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Hybrid Model of Convolutional Neural Network and Support Vector Machine to Classify Basal Cell Carcinoma

Cited by 7 publications

References 3 publications

Automated Analysis of Nuclear Parameters in Oral Exfoliative Cytology Using Machine Learning

Automated Analysis of Nuclear Parameters in Oral Exfoliative Cytology Using Machine Learning

Automated Malignant Melanoma Classification Using Convolutional Neural Networks

Hybrid Convolutional Neural Network and Support Vector Machine Based on Model Optimization for Skin Cancer Classification

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