An Evaluation Performance of Kernel on Support Vector Machine to Classify The Skin Tumors in Dermoscopy Image

Rahajeng, Andhryn Celica Dewi; Nuh, Mohammad; Hikmah, Nada Fitrieyatul

doi:10.1109/cenim51130.2020.9297941

Cited by 2 publications

(1 citation statement)

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“…Gradient Boosted Tree (GBT) gave 97.5% accuracy. In [ 26 ], Rahajeng, M. Nuh used different techniques, such as median filter, threshold and automatic cropping as image pre-processing operations. Furthermore, active contour and Sobel filters were applied for skin lesion segmentation based on shape, color, and texture features, which were obtained using the GLCM method.…”

Section: Related Workmentioning

confidence: 99%

Skin Lesion Detection Using Hand-Crafted and DL-Based Features Fusion and LSTM

Mahum

Aladhadh

2022

Diagnostics

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The abnormal growth of cells in the skin causes two types of tumor: benign and malignant. Various methods, such as imaging and biopsies, are used by oncologists to assess the presence of skin cancer, but these are time-consuming and require extra human effort. However, some automated methods have been developed by researchers based on hand-crafted feature extraction from skin images. Nevertheless, these methods may fail to detect skin cancers at an early stage if they are tested on unseen data. Therefore, in this study, a novel and robust skin cancer detection model was proposed based on features fusion. First, our proposed model pre-processed the images using a GF filter to remove the noise. Second, the features were manually extracted by employing local binary patterns (LBP), and Inception V3 for automatic feature extraction. Aside from this, an Adam optimizer was utilized for the adjustments of learning rate. In the end, LSTM network was utilized on fused features for the classification of skin cancer into malignant and benign. Our proposed system employs the benefits of both ML- and DL-based algorithms. We utilized the skin lesion DermIS dataset, which is available on the Kaggle website and consists of 1000 images, out of which 500 belong to the benign class and 500 to the malignant class. The proposed methodology attained 99.4% accuracy, 98.7% precision, 98.66% recall, and a 98% F-score. We compared the performance of our features fusion-based method with existing segmentation-based and DL-based techniques. Additionally, we cross-validated the performance of our proposed model using 1000 images from International Skin Image Collection (ISIC), attaining 98.4% detection accuracy. The results show that our method provides significant results compared to existing techniques and outperforms them.

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Section: Related Workmentioning

confidence: 99%

Skin Lesion Detection Using Hand-Crafted and DL-Based Features Fusion and LSTM

Mahum

Aladhadh

2022

Diagnostics

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SCDet: A Robust Approach for the Detection of Skin Lesions

et al. 2023

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Red, blue, white, pink, or black spots with irregular borders and small lesions on the skin are known as skin cancer that is categorized into two types: benign and malignant. Skin cancer can lead to death in advanced stages, however, early detection can increase the chances of survival of skin cancer patients. There exist several approaches developed by researchers to identify skin cancer at an early stage, however, they may fail to detect the tiniest tumours. Therefore, we propose a robust method for the diagnosis of skin cancer, namely SCDet, based on a convolutional neural network (CNN) having 32 layers for the detection of skin lesions. The images, having a size of 227 × 227, are fed to the image input layer, and then pair of convolution layers is utilized to withdraw the hidden patterns of the skin lesions for training. After that, batch normalization and ReLU layers are used. The performance of our proposed SCDet is computed using the evaluation matrices: precision 99.2%; recall 100%; sensitivity 100%; specificity 99.20%; and accuracy 99.6%. Moreover, the proposed technique is compared with the pre-trained models, i.e., VGG16, AlexNet, and SqueezeNet and it is observed that SCDet provides higher accuracy than these pre-trained models and identifies the tiniest skin tumours with maximum precision. Furthermore, our proposed model is faster than the pre-trained model as the depth of its architecture is not too high as compared to pre-trained models such as ResNet50. Additionally, our proposed model consumes fewer resources during training; therefore, it is better in terms of computational cost than the pre-trained models for the detection of skin lesions.

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An Evaluation Performance of Kernel on Support Vector Machine to Classify The Skin Tumors in Dermoscopy Image

Cited by 2 publications

References 10 publications

Skin Lesion Detection Using Hand-Crafted and DL-Based Features Fusion and LSTM

Skin Lesion Detection Using Hand-Crafted and DL-Based Features Fusion and LSTM

SCDet: A Robust Approach for the Detection of Skin Lesions

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