hybSVM: Bacterial colony optimization algorithm based SVM for malignant melanoma detection

İlkin, Sümeyya; Gençtürk, Tuğrul Hakan; Gülağız, Fidan Kaya; Özcan, Hikmetcan; Altuncu, Mehmet Ali; Şahin, Sühap

doi:10.1016/j.jestch.2021.02.002

“…The classification model was evaluated on the ISIC 2018 dataset, achieving a 76% accuracy with an F1-score of 75%. The SVM algorithm was enhanced in [ 20 ] through a heuristic optimisation algorithm to develop a hybrid classification method named hybSVM. The new algorithm targets optimising the gamma and C value to improve the operating cost.…”

Section: Related Literaturementioning

confidence: 99%

Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma

Bandy

¹

,

Spyridis

²

,

Villarini

³

et al. 2023

Sensors

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This paper describes the process of developing a classification model for the effective detection of malignant melanoma, an aggressive type of cancer in skin lesions. Primary focus is given on fine-tuning and improving a state-of-the-art convolutional neural network (CNN) to obtain the optimal ROC-AUC score. The study investigates a variety of artificial intelligence (AI) clustering techniques to train the developed models on a combined dataset of images across data from the 2019 and 2020 IIM-ISIC Melanoma Classification Challenges. The models were evaluated using varying cross-fold validations, with the highest ROC-AUC reaching a score of 99.48%.

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“…I ˙lkin et al [65] used the SVM algorithm as a classifier that utilizes a Gaussian radial basis function which had been enhanced by the bacterial colony algorithm. The proposed model was trained and evaluated using two datasets, namely ISIC and PH2.…”

Section: Classical Machine Learning-based Approachesmentioning

confidence: 99%

Skin cancer diagnosis based on deep transfer learning and sparrow search algorithm

Balaha

¹

,

Hassan

²

2022

Neural Comput & Applic

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Skin cancer affects the lives of millions of people every year, as it is considered the most popular form of cancer. In the USA alone, approximately three and a half million people are diagnosed with skin cancer annually. The survival rate diminishes steeply as the skin cancer progresses. Despite this, it is an expensive and difficult procedure to discover this cancer type in the early stages. In this study, a threshold-based automatic approach for skin cancer detection, classification, and segmentation utilizing a meta-heuristic optimizer named sparrow search algorithm (SpaSA) is proposed. Five U-Net models (i.e., U-Net, U-Net++, Attention U-Net, V-net, and Swin U-Net) with different configurations are utilized to perform the segmentation process. Besides this, the meta-heuristic SpaSA optimizer is used to perform the optimization of the hyperparameters using eight pre-trained CNN models (i.e., VGG16, VGG19, MobileNet, MobileNetV2, MobileNetV3Large, MobileNetV3Small, NASNetMobile, and NASNetLarge). The dataset is gathered from five public sources in which two types of datasets are generated (i.e., 2-classes and 10-classes). For the segmentation, concerning the “skin cancer segmentation and classification” dataset, the best reported scores by U-Net++ with DenseNet201 as a backbone architecture are 0.104, $$94.16\%$$ 94.16 % , $$91.39\%$$ 91.39 % , $$99.03\%$$ 99.03 % , $$96.08\%$$ 96.08 % , $$96.41\%$$ 96.41 % , $$77.19\%$$ 77.19 % , $$75.47\%$$ 75.47 % in terms of loss, accuracy, F1-score, AUC, IoU, dice, hinge, and squared hinge, respectively, while for the “PH2” dataset, the best reported scores by the Attention U-Net with DenseNet201 as backbone architecture are 0.137, $$94.75\%$$ 94.75 % , $$92.65\%$$ 92.65 % , $$92.56\%$$ 92.56 % , $$92.74\%$$ 92.74 % , $$96.20\%$$ 96.20 % , $$86.30\%$$ 86.30 % , $$92.65\%$$ 92.65 % , $$69.28\%$$ 69.28 % , and $$68.04\%$$ 68.04 % in terms of loss, accuracy, F1-score, precision, sensitivity, specificity, IoU, dice, hinge, and squared hinge, respectively. For the “ISIC 2019 and 2020 Melanoma” dataset, the best reported overall accuracy from the applied CNN experiments is $$98.27\%$$ 98.27 % by the MobileNet pre-trained model. Similarly, for the “Melanoma Classification (HAM10K)” dataset, the best reported overall accuracy from the applied CNN experiments is $$98.83\%$$ 98.83 % by the MobileNet pre-trained model. For the “skin diseases image” dataset, the best reported overall accuracy from the applied CNN experiments is $$85.87\%$$ 85.87 % by the MobileNetV2 pre-trained model. After computing the results, the suggested approach is compared with 13 related studies.

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“…al. [37], the SVM classifier and a bacterial colony optimization algorithm were integrated to create a hybrid classification to detect melanoma accurately. The proposed technique was tested on two separate datasets, ISIC and PH2.…”

Section: Hybrid Techniquesmentioning

confidence: 99%

An Aseptic Approach Towards Skin Lesion Localization and Grading using Deep Learning and Harris Hawks Optimization

Balaha

¹

,

Hassan

²

,

El-Gendy

³

et al. 2022

Preprint

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Skin cancer is the most common form of cancer. Hence, the lives of millions of people are affected by this cancer every year. Approximately, it is predicted that the total number of cases of cancer will double in the next fifty years. It is an expensive procedure to discover skin cancer types in the early stages. Additionally, the survival rate reduces as cancer progresses. The current study proposes an aseptic approach toward skin lesion detection, classification, and segmentation using deep learning and a meta-heuristic optimizer called Harris Hawks Optimization Algorithm (HHO). The current study utilized the manual and automatic segmentation approaches. The manual segmentation is used when the dataset has no masks to use while the automatic segmentation approach is used, using U-Net models, to build an adaptive segmentation model. Additionally, the meta-heuristic HHO optimizer is utilized to achieve the optimization of the hyperparameters using 5 pre-trained CNN models (i.e., VGG16, VGG19, DenseNet169, DenseNet201, and MobileNet). Two are collected (i.e., "Melanoma Skin Cancer Dataset of 10000 Images" and "Skin Cancer ISIC" dataset) from two publically available sources. For the segmentation, the best-reported scores are 0.15908, 91.95%, 0.08864, 0.04313, 0.02072, 0.20767 in terms of loss, accuracy, Mean Absolute Error, Mean Squared Error, Mean Squared Logarithmic Error, and Root Mean Squared Error, respectively. For the "Melanoma Skin Cancer Dataset of 10000 Images" dataset, from the applied experiments, the best reported overall accuracy is 97.08% by the DenseNet169 pre-trained model. For the "Skin Cancer ISIC" dataset, the best reported overall accuracy is 96.06% by the MobileNet pre-trained model. After computing the results, the suggested approach is compared with 9 related studies.

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hybSVM: Bacterial colony optimization algorithm based SVM for malignant melanoma detection

Cited by 23 publications

References 31 publications

Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma

Intraclass Clustering-Based CNN Approach for Detection of Malignant Melanoma

Skin cancer diagnosis based on deep transfer learning and sparrow search algorithm

An Aseptic Approach Towards Skin Lesion Localization and Grading using Deep Learning and Harris Hawks Optimization

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