An accurate and <scp>noninvasive</scp> skin cancer screening based on imaging technique

Rajput, Gunjan; Agrawal, Shashank; Raut, Gopal; Vishvakarma, Santosh Kumar

doi:10.1002/ima.22616

Cited by 28 publications

(10 citation statements)

References 40 publications

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“…The Austrian site started collecting images with pre-digital cameras and preserved them in several formats. Based on the research, a variety of approaches are endorsed [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 ]. Using data from this benchmark, the Resnet-50 and the suggested CNN are trained to identify skin cancer in this study.…”

Section: Methodsmentioning

confidence: 99%

Melanoma Detection Using Deep Learning-Based Classifications

et al. 2022

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One of the most prevalent cancers worldwide is skin cancer, and it is becoming more common as the population ages. As a general rule, the earlier skin cancer can be diagnosed, the better. As a result of the success of deep learning (DL) algorithms in other industries, there has been a substantial increase in automated diagnosis systems in healthcare. This work proposes DL as a method for extracting a lesion zone with precision. First, the image is enhanced using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to improve the image’s quality. Then, segmentation is used to segment Regions of Interest (ROI) from the full image. We employed data augmentation to rectify the data disparity. The image is then analyzed with a convolutional neural network (CNN) and a modified version of Resnet-50 to classify skin lesions. This analysis utilized an unequal sample of seven kinds of skin cancer from the HAM10000 dataset. With an accuracy of 0.86, a precision of 0.84, a recall of 0.86, and an F-score of 0.86, the proposed CNN-based Model outperformed the earlier study’s results by a significant margin. The study culminates with an improved automated method for diagnosing skin cancer that benefits medical professionals and patients.

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Section: Methodsmentioning

confidence: 99%

Melanoma Detection Using Deep Learning-Based Classifications

et al. 2022

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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%

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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“…Chaturvedi et al 28 designed a CNN‐based ensemble approach for multi‐class skin cancer classification. A customized AlexNet architecture with new activation function was presented in Rajput et al 29 for accurate skin cancer classification. In Pacheco and Krohling, 30 an attention‐based technique with metadata processing block has been designed in DL models for skin cancer classification.…”

Section: Related Workmentioning

confidence: 99%

Patch‐based local deep feature extraction for automated skin cancer classification

Gajera

Zaveri

Nayak

2022

Int J Imaging Syst Tech

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Skin cancer detection through dermoscopy images has remained challenging due to higher inter‐class uniformity and intra‐class diversity. Deep convolutional neural networks (CNNs) have recently obtained remarkable attention for automated skin cancer classification; however, most of these methods extract features from the global image of high resolution. One of the major drawbacks of these methods is the downscaling of input images to a low resolution, which leads to information loss. Moreover, the lack of a vast number of dermoscopy images is a concern. To overcome these issues and improve the classification accuracy, we propose a novel method using patch‐based local deep feature extraction. In the proposed method, the features are extracted from different patches of a dermoscopy image using a pre‐trained CNN model and are fused to preserve fine details. The kernel principal component analysis is then employed to select the prominent features, and a feed‐forward neural network is finally used to detect skin cancer. The approach is validated using two benchmark datasets: ISIC 2016 and ISIC 2017. The experimental results show that the suggested method achieves promising results in comparison with state‐of‐the‐art approaches. Moreover, a comparative analysis is made among contemporary pre‐trained CNN models.

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An accurate and noninvasive skin cancer screening based on imaging technique

Cited by 28 publications

References 40 publications

Melanoma Detection Using Deep Learning-Based Classifications

Melanoma Detection Using Deep Learning-Based Classifications

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

Patch‐based local deep feature extraction for automated skin cancer classification

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