SkNet: A Convolutional Neural Networks Based Classification Approach for Skin Cancer Classes

Jeny, Afsana Ahsan; Sakib, Abu Noman Md; Junayed, Masum Shah; Lima, Khadija Akter; Ahmed, Ikhtiar; Islam, Md Baharul

doi:10.1109/iccit51783.2020.9392716

Cited by 14 publications

(8 citation statements)

References 11 publications

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“…The dermoscopy images in the dataset have a resolution of 450 × 600 pixels. To reconcile the images with the input of the model we downscaled the resolution of images to 224 × 224 pixels [ 54 ]. Moreover, the data normalization technique was also used to ensure that the proposed approach is properly trained [ 55 ].…”

Section: Proposed Methodologymentioning

confidence: 99%

SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images

Naeem

Anees

Fiza

et al. 2022

Sensors

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Skin cancer is a deadly disease, and its early diagnosis enhances the chances of survival. Deep learning algorithms for skin cancer detection have become popular in recent years. A novel framework based on deep learning is proposed in this study for the multiclassification of skin cancer types such as Melanoma, Melanocytic Nevi, Basal Cell Carcinoma and Benign Keratosis. The proposed model is named as SCDNet which combines Vgg16 with convolutional neural networks (CNN) for the classification of different types of skin cancer. Moreover, the accuracy of the proposed method is also compared with the four state-of-the-art pre-trained classifiers in the medical domain named Resnet 50, Inception v3, AlexNet and Vgg19. The performance of the proposed SCDNet classifier, as well as the four state-of-the-art classifiers, is evaluated using the ISIC 2019 dataset. The accuracy rate of the proposed SDCNet is 96.91% for the multiclassification of skin cancer whereas, the accuracy rates for Resnet 50, Alexnet, Vgg19 and Inception-v3 are 95.21%, 93.14%, 94.25% and 92.54%, respectively. The results showed that the proposed SCDNet performed better than the competing classifiers.

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Section: Proposed Methodologymentioning

confidence: 99%

SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images

Naeem

Anees

Fiza

et al. 2022

Sensors

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“…In future, the best filtering and feature extraction research options from the images need to be explored to make sure the prediction accuracy is increased. CNN with filtering and feature extraction [8] 96.39% [11] 98.43% [12] 92.00% [13] 95.26% [14] 91.00% [15] 90.48% [16] 92.30% [17] 98.44% [18] 99.33%…”

Section: Discussionmentioning

confidence: 99%

“…A classification method proposed by Jeny et al [13] uses novel CNN based approach to classify the different types of skin cancers. This CNN developed by using SKNet which consists of 19 Convolution layers.…”

Section: Cnn With Filtering and Feature Extraction Techniquesmentioning

confidence: 99%

Convolution Neural Network Models to Detect Melanoma: A Review

Naveen¹,

Kumar²,

Haw³

et al. 2022

Proceedings of the International Conference on Computer, Information Technology and Intelligent Computing (CITIC 2022)

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Skin cancer is one of the most serious health issues that humans face. Dermatologists face difficulty in making a skin cancer diagnosis because many skin cancer pigments seem alike. Early detection of skin cancers like Melanoma means a better chance of survival for the patient otherwise it can be life-threatening. For computer vision problems like image classification, deep learning has proven to be the state-of-the-art. There has been a great deal of research into the use of deep learning to automate skin cancer screening. The objective of this paper is to review the state-of-the-art CNN techniques used for Melanoma detection. This paper presents an overview of CNN, followed by analysing the existing work carried out in the area of Melanoma skin cancer detection using Convolution Neural Network (CNN).

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“…SKNet and SENet are lightweight modules that can be directly embedded in the network. Feature maps of different convolution kernelsizes are fused through the Attention Mechanism, and the size of the Attention is based on the deterministic information extracted by convolution kernels of different sizes [39]. Intuitively, SKNet assimilate a soft attention mechanism into the network, so that the network can obtain information of different Receptive Field, which may become a network structure with better generalization ability [40].…”

Section: Model Trainingmentioning

confidence: 99%

Classification of Multi-Differentiated Liver Cancer Pathological Images Based on Deep Learning Attention Mechanism

Chen

et al. 2022

Preprint

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Purpose Liver cancer is one of the most common malignant tumors in the world, ranking fifth in malignant tumors. The degree of differentiation can reflect the degree of malignancy. The degree of malignancy of liver cancer can be divided into three types: poorly differentiated, moderately differentiated, and well differentiated. Diagnosis and treatment of different levels of differentiation are crucial to the survival rate and survival time of patients. As the gold standard for liver cancer diagnosis, histopathological images can accurately distinguish liver cancers of different levels of differentiation. Therefore, the study of intelligent classification of histopathological images is of great significance to patients with liver cancer. At present, the classification of histopathological images of liver cancer with different degrees of differentiation has disadvantages such as time-consuming, labor-intensive, and large manual investment. In this context, the importance of intelligent classification of histopathological images is obvious. Methods Based on the development of a complete data acquisition scheme, this paper applies the SENet deep learning model to the intelligent classification of all types of differentiated liver cancer histopathological images for the first time, and compares it with the four deep learning models of VGG16, ResNet50, ResNet_CBAM, and SKNet. The evaluation indexes adopted in this paper include confusion matrix, Precision, recall, F1 Score, etc. These evaluation indexes can be used to evaluate the model in a very comprehensive and accurate way. Results Five different deep learning classification models are applied to collect the data set and evaluate model. The experimental results show that the SENet model has achieved the best classification effect with an accuracy of 95.27%. The model also has good reliability and generalization ability. The experiment proves that the SENet deep learning model has a good application prospect in the intelligent classification of histopathological images, Conclusions This study also proves that deep learning has great application value in solving the time-consuming and laborious problems existing in traditional manual film reading, and it has certain practical significance for the intelligent classification research of other cancer histopathological images.

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SkNet: A Convolutional Neural Networks Based Classification Approach for Skin Cancer Classes

Cited by 14 publications

References 11 publications

SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images

SCDNet: A Deep Learning-Based Framework for the Multiclassification of Skin Cancer Using Dermoscopy Images

Convolution Neural Network Models to Detect Melanoma: A Review

Classification of Multi-Differentiated Liver Cancer Pathological Images Based on Deep Learning Attention Mechanism

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