InSiNet: a deep convolutional approach to skin cancer detection and segmentation

Reis, Hatice Çatal; Turk, Veysel; Khoshelham, Kourosh; Kaya, Serhat

doi:10.1007/s11517-021-02473-0

Cited by 60 publications

(30 citation statements)

References 45 publications

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“…Data Set Techniques Used Number of Classes [25] 300 HAM10000 CNN with XGBoost Five [26] 1323 HAM10000 InSiNet Two [27] [33] 7470 HAM10000 ResNet50 Seven [34] 3753 ImageNet ECOC SVM Two [35] 16,170 HAM10000 Anisotropic diffusion filtering Two [36] 1000 ISIC SVM + RF Eight [37] 6705 HAM10000 DCNN Two [38] 279 ImageNet DCNN VGG-16 Two [39] 10,015 HAM10000 AlexNet Seven [40] 10,015 HAM10000 CNN Seven Timely screening and prediction have been found to enhance the probability of proper medication and reduce mortality. However, most of these studies focused solely on applying DL models to actual images rather than preprocessed images, limiting the ultimate classification network's ability to adapt.…”

Section: Recent Work Data Sizementioning

confidence: 99%

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Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning

et al. 2022

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An increasing number of genetic and metabolic anomalies have been determined to lead to cancer, generally fatal. Cancerous cells may spread to any body part, where they can be life-threatening. Skin cancer is one of the most common types of cancer, and its frequency is increasing worldwide. The main subtypes of skin cancer are squamous and basal cell carcinomas, and melanoma, which is clinically aggressive and responsible for most deaths. Therefore, skin cancer screening is necessary. One of the best methods to accurately and swiftly identify skin cancer is using deep learning (DL). In this research, the deep learning method convolution neural network (CNN) was used to detect the two primary types of tumors, malignant and benign, using the ISIC2018 dataset. This dataset comprises 3533 skin lesions, including benign, malignant, nonmelanocytic, and melanocytic tumors. Using ESRGAN, the photos were first retouched and improved. The photos were augmented, normalized, and resized during the preprocessing step. Skin lesion photos could be classified using a CNN method based on an aggregate of results obtained after many repetitions. Then, multiple transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were used for fine-tuning. In addition to experimenting with several models (the designed CNN, Resnet50, InceptionV3, and Inception Resnet), this study’s innovation and contribution are the use of ESRGAN as a preprocessing step. Our designed model showed results comparable to the pretrained model. Simulations using the ISIC 2018 skin lesion dataset showed that the suggested strategy was successful. An 83.2% accuracy rate was achieved by the CNN, in comparison to the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models.

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Section: Recent Work Data Sizementioning

confidence: 99%

“…Man and machine obtained 82.95% multiclass accuracy [ 25 ]. The deep learning-based InSiNet technique detects benign and malignant tumors [ 26 ]. Under similar scenarios, the approach was evaluated on HAM10000 images (ISIC 2018), ISIC 2019, and ISIC 2020.…”

Section: Related Workmentioning

confidence: 99%

Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning

et al. 2022

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“…Using ISIC datasets, Hatice Catal Reis et al. ( 24 ) also developed a deep learning-based convolutional neural network (CNN) model to detect benign and malignant lesions, but they incorporated International Skin Imaging Collaboration HAM10000 images (ISIC 2018), ISIC 2019, and ISIC 2020 datasets. This model was developed based on the Inception module used in GoogleNet architecture, and it used fewer parameters and fewer medical images to make the diagnostic time shorter.…”

Section: Literature Reviewmentioning

confidence: 99%

SkinNet-16: A deep learning approach to identify benign and malignant skin lesions

et al. 2022

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Skin cancer these days have become quite a common occurrence especially in certain geographic areas such as Oceania. Early detection of such cancer with high accuracy is of utmost importance, and studies have shown that deep learning- based intelligent approaches to address this concern have been fruitful. In this research, we present a novel deep learning- based classifier that has shown promise in classifying this type of cancer on a relevant preprocessed dataset having important features pre-identified through an effective feature extraction method.Skin cancer in modern times has become one of the most ubiquitous types of cancer. Accurate identification of cancerous skin lesions is of vital importance in treating this malady. In this research, we employed a deep learning approach to identify benign and malignant skin lesions. The initial dataset was obtained from Kaggle before several preprocessing steps for hair and background removal, image enhancement, selection of the region of interest (ROI), region-based segmentation, morphological gradient, and feature extraction were performed, resulting in histopathological images data with 20 input features based on geometrical and textural features. A principle component analysis (PCA)-based feature extraction technique was put into action to reduce the dimensionality to 10 input features. Subsequently, we applied our deep learning classifier, SkinNet-16, to detect the cancerous lesion accurately at a very early stage. The highest accuracy was obtained with the Adamax optimizer with a learning rate of 0.006 from the neural network-based model developed in this study. The model also delivered an impressive accuracy of approximately 99.19%.

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“…The initial step is to choose a model architecture. Deep CNNs are frequently used in image classification applications (Reis et al, 2022). The model already includes the entire lines and forms required for image recognition.…”

Section: Deep Learning Strategiesmentioning

confidence: 99%

A Comprehensive Review on Skin Cancer Detection Strategies using Deep Neural Networks

Reddy¹,

P²

2022

Journal of Computer Science

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Skin cancer is a deadly malignancy. Incomplete D.N.A. repair in skin cells causes hereditary mutations and cancer. Early skin cancer is easier to treat since it spreads slowly to other body areas. As a result, the optimal time to find it is during its infancy. Because of the rising frequency of skin cancer, the high mortality rate, and the high cost of medical treatment, early detection of skin cancer symptoms is essential. Researchers have created a variety of early detection techniques for skin cancer due to these obstacles. A lesion's symmetry, coloration, size, and shape help doctors identify and differentiate between skin cancer and melanoma. These considerations prompted the researcher to do research into automated skin cancer diagnosis. The use of machine learning is quickly becoming one of the most promising approaches to the early detection and treatment of skin cancer. A recent study demonstrated the ability of deep network topologies to segment and analyzes skin cancer. According to the findings of this study, further investigation into the application of Deep Learning (DL) algorithms for the early detection of skin cancer is required. An investigation into significant research articles on skin cancer diagno sis that have been published in reputable journals was carried out.

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InSiNet: a deep convolutional approach to skin cancer detection and segmentation

Cited by 60 publications

References 45 publications

Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning

Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning

SkinNet-16: A deep learning approach to identify benign and malignant skin lesions

A Comprehensive Review on Skin Cancer Detection Strategies using Deep Neural Networks

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