Melanoma Detection Using Convolutional Neural Network

Zhang, Runyuan

doi:10.1109/iccece51280.2021.9342142

Cited by 20 publications

(8 citation statements)

References 10 publications

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“…This study first uses the texture features of glands to identify the presence of individual glandular structures; then, the texture features and morphometric obtained from glandular units are applied to the classification stage, and finally the images are labeled as grades 1 to 5 [ 5 ]. The literature shows that the texture features of the image are represented according to the different power spectra of the image [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network

Tyagi

Tyagi²,

Choudhury³

et al. 2022

BioMed Research International

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Prostate cancer is one of the most common cancers in men worldwide, second only to lung cancer. The most common method used in diagnosing prostate cancer is the microscopic observation of stained biopsies by a pathologist and the Gleason score of the tissue microarray images. However, scoring prostate cancer tissue microarrays by pathologists using Gleason mode under many tissue microarray images is time-consuming, susceptible to subjective factors between different observers, and has low reproducibility. We have used the two most common technologies, deep learning, and computer vision, in this research, as the development of deep learning and computer vision has made pathology computer-aided diagnosis systems more objective and repeatable. Furthermore, the U-Net network, which is used in our study, is the most extensively used network in medical image segmentation. Unlike the classifiers used in previous studies, a region segmentation model based on an improved U-Net network is proposed in our research, which fuses deep and shallow layers through densely connected blocks. At the same time, the features of each scale are supervised. As an outcome of the research, the network parameters can be reduced, the computational efficiency can be improved, and the method’s effectiveness is verified on a fully annotated dataset.

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Section: Introductionmentioning

confidence: 99%

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network

Tyagi

Tyagi²,

Choudhury³

et al. 2022

BioMed Research International

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“…Zhang [8], presented the EfficientNet-B6 model for melanoma detection on the ISIC dataset. To assess model performance, the proposed model was compared with other standard models like VGG16 and VGG19.…”

Section: Related Workmentioning

confidence: 99%

Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3

Salian

Sawarkar

2023

Adv. technol. innov.

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The rise of incidences of melanoma skin cancer is a global health problem. Skin cancer, if diagnosed at an early stage, enhances the chances of a patient’s survival. Building an automated and effective melanoma classification system is the need of the hour. In this paper, an automated computer-based diagnostic system for melanoma skin lesion classification is presented using fine-tuned EfficientNetB3 model over ISIC 2017 dataset. To improve classification results, an automated image pre-processing phase is incorporated in this study, it can effectively remove noise artifacts such as hair structures and ink markers from dermoscopic images. Comparative analyses of various advanced models like ResNet50, InceptionV3, InceptionResNetV2, and EfficientNetB0-B2 are conducted to corroborate the performance of the proposed model. The proposed system also addressed the issue of model overfitting and achieved a precision of 88.00%, an accuracy of 88.13%, recall of 88%, and F1-score of 88%.

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“…Our approach shares the standard backbone setting [17,18,19] consisting of a color constancy layer for image preprocessing and an encoder for feature extraction.…”

Section: Backbone Settingmentioning

confidence: 99%

“…Feature extraction. Feature extraction via supervised learning is widely employed in dermoscopic image classification tasks [18,19]. Following the previous work in [22], we utilizes EfficientNet-B3 [23] to extract the lesion features F e , formulated by:…”

Section: Backbone Settingmentioning

confidence: 99%

Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification

Tang

Yang

et al. 2021

Neural Information Processing

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Deep learning-based melanoma classification with dermoscopic images has recently shown great potential in automatic earlystage melanoma diagnosis. However, limited by the significant data imbalance and obvious extraneous artifacts, i.e., the hair and ruler markings, discriminative feature extraction from dermoscopic images is very challenging. In this study, we seek to resolve these problems respectively towards better representation learning for lesion features. Specifically, a GAN-based data augmentation (GDA) strategy is adapted to generate synthetic melanoma-positive images, in conjunction with the proposed implicit hair denoising (IHD) strategy. Wherein the hair-related representations are implicitly disentangled via an auxiliary classifier network and reversely sent to the melanoma-feature extraction backbone for better melanoma-specific representation learning. Furthermore, to train the IHD module, the hair noises are additionally labeled on the ISIC2020 dataset, making it the first large-scale dermoscopic dataset with annotation of hair-like artifacts. Extensive experiments demonstrate the superiority of the proposed framework as well as the effectiveness of each component. The improved dataset publicly avaliable at https://github.com/kirtsy/DermoscopicDataset.

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Melanoma Detection Using Convolutional Neural Network

Cited by 20 publications

References 10 publications

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network

Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network

Skin Lesion Classification towards Melanoma Detection Using EfficientNetB3

Towards Better Dermoscopic Image Feature Representation Learning for Melanoma Classification

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