Melanoma Detection using Advanced Deep Neural Network

Sharma, Pallabi; Gautam, Anmol; Nayak, Rajashree; Balabantaray, Bunil Kumar

doi:10.1109/icepe55035.2022.9798123

Cited by 9 publications

(3 citation statements)

References 8 publications

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“…They remove the output layer from these architectures and add pooling and fully connected layers. In [23], new adversarial example images are created using the fast gradient sign method (FGSM), and some pre-trained networks (VGG16, VGG19, DenseNet101, and ResNet101) are trained and tested with them. Adversarial training allows maximizing the loss for the input image.…”

Section: Skin Lesion Classification With Deep Learning Methodsmentioning

confidence: 99%

Anomaly Detection for Skin Lesion Images Using Convolutional Neural Network and Injection of Handcrafted Features: A Method That Bypasses the Preprocessing of Dermoscopic Images

Grignaffini,

Troiano,

Barbuto

et al. 2023

Algorithms

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Skin cancer (SC) is one of the most common cancers in the world and is a leading cause of death in humans. Melanoma (M) is the most aggressive form of skin cancer and has an increasing incidence rate. Early and accurate diagnosis of M is critical to increase patient survival rates; however, its clinical evaluation is limited by the long timelines, variety of interpretations, and difficulty in distinguishing it from nevi (N) because of striking similarities. To overcome these problems and to support dermatologists, several machine-learning (ML) and deep-learning (DL) approaches have been developed. In the proposed work, melanoma detection, understood as an anomaly detection task with respect to the normal condition consisting of nevi, is performed with the help of a convolutional neural network (CNN) along with the handcrafted texture features of the dermoscopic images as additional input in the training phase. The aim is to evaluate whether the preprocessing and segmentation steps of dermoscopic images can be bypassed while maintaining high classification performance. Network training is performed on the ISIC2018 and ISIC2019 datasets, from which only melanomas and nevi are considered. The proposed network is compared with the most widely used pre-trained networks in the field of dermatology and shows better results in terms of classification and computational cost. It is also tested on the ISIC2016 dataset to provide a comparison with the literature: it achieves high performance in terms of accuracy, sensitivity, and specificity.

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Section: Skin Lesion Classification With Deep Learning Methodsmentioning

confidence: 99%

Anomaly Detection for Skin Lesion Images Using Convolutional Neural Network and Injection of Handcrafted Features: A Method That Bypasses the Preprocessing of Dermoscopic Images

Grignaffini,

Troiano,

Barbuto

et al. 2023

Algorithms

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show abstract

“…In [97], adversarial training is used to achieve good accuracy in skin tumour classification, despite having a small amount of data available. By applying the fast gradient sign method (FGSM), new adversarial example images are created to maximise the loss for the input image, which are subsequently used in both train and test phases.…”

Section: Deep-learning Methodsmentioning

confidence: 99%

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

et al. 2022

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Skin cancer (SC) is one of the most prevalent cancers worldwide. Clinical evaluation of skin lesions is necessary to assess the characteristics of the disease; however, it is limited by long timelines and variety in interpretation. As early and accurate diagnosis of SC is crucial to increase patient survival rates, machine-learning (ML) and deep-learning (DL) approaches have been developed to overcome these issues and support dermatologists. We present a systematic literature review of recent research on the use of machine learning to classify skin lesions with the aim of providing a solid starting point for researchers beginning to work in this area. A search was conducted in several electronic databases by applying inclusion/exclusion filters and for this review, only those documents that clearly and completely described the procedures performed and reported the results obtained were selected. Sixty-eight articles were selected, of which the majority use DL approaches, in particular convolutional neural networks (CNN), while a smaller portion rely on ML techniques or hybrid ML/DL approaches for skin cancer detection and classification. Many ML and DL methods show high performance as classifiers of skin lesions. The promising results obtained to date bode well for the not-too-distant inclusion of these techniques in clinical practice.

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“…The results of the paper show that the proposed SPGGAN and TTUR can lead to statistically significant improvements in the sensitivity (recall) over nonaugmented and augmented counterparts, with classical data augmentation, for all classes and specifically for melanoma class. Further, a novel method for melanoma skin cancer using an advanced deep neural network and adversarial training to achieve better accuracy even with a small amount of data [42]. The input image's depth, gradient, and shade are amplified to extract useful information.…”

Section: Melanoma Image Synthesis Using Gansmentioning

confidence: 99%

Melanoma image synthesis: a review using generative adversarial networks

Ahmed,

Qureshi,

Umar

et al. 2024

IJEECS

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Melanoma is a highly malignant skin cancer that may be fatal if not promptly detected and treated. The limited availability of high-quality melanoma images, which are needed for training machine learning models, is one of the obstacles to detecting melanoma. Generative adversarial networks (GANs) have grown in popularity as a strong technique for image synthesis. This research is also targeted at the sustainable development goal (SDG) for health care. In this study, we survey existing GAN-based melanoma image synthesis methods. In this work, we briefly introduce GANs and how they may be used for generating synthetic images. Ensuring healthy lifestyles and promoting well-being for everyone, regardless of age, is the main aim. A comparative study is carried out on how GANs are used in current research to generate melanoma images and how they improve the classification performance of neural networks. Various public and proprietary datasets for training GANs in melanoma image synthesis are also discussed. Lastly, we assess the examined studies' performance using measures like the Frechet Inception distance (FID), Inception score, structural similarity ındex (SSIM), and various classification performance metrics. We compare the evaluated findings and suggest further GAN-based melanoma image-creation research.

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Melanoma Detection using Advanced Deep Neural Network

Cited by 9 publications

References 8 publications

Anomaly Detection for Skin Lesion Images Using Convolutional Neural Network and Injection of Handcrafted Features: A Method That Bypasses the Preprocessing of Dermoscopic Images

Anomaly Detection for Skin Lesion Images Using Convolutional Neural Network and Injection of Handcrafted Features: A Method That Bypasses the Preprocessing of Dermoscopic Images

Machine Learning Approaches for Skin Cancer Classification from Dermoscopic Images: A Systematic Review

Melanoma image synthesis: a review using generative adversarial networks

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