Effect of image noise on the classification of skin lesions using deep convolutional neural networks

Abstract

Skin lesions are in a category of disease that is both common in humans and a major cause of death. The classification accuracy of skin lesions is a crucial determinant of the success rate of curing lethal diseases. Deep Convolutional Neural Networks (CNNs) are now the most prevalent computer algorithms for the purpose of disease classification. As with all algorithms, CNNs are sensitive to noise from imaging devices, which often contaminates the quality of the images that are fed into them. In this paper, a deep CNN (Inception-v3) is used to study the effect of image noise on the classification of skin lesions. Gaussian noise, impulse noise, and noise made up of a compound of the two are added to an image dataset, namely the Dermofit Image Library from the University of Edinburgh. Evaluations, based on t-distributed Stochastic Neighbor Embedding (t-SNE) visualization, Receiver Operating Characteristic (ROC) analysis, and saliency maps, demonstrate the reliability of the Inception-v3 deep CNN in classifying noisy skin lesion images.

Deep Learning in Skin Disease Image Recognition: A Review

“…However, Non-neoplastic skin diseases are scarce, and only three articles on deep learning of eczema and psoriasis identification [46], [51], and [74] are collected in this study. [44], [45], [47], [48], [50], [52], [53], [54], [55], [56], [57], [60], [61], [64], [65], [76], [77], [78], [79], [80], [82], and [84] Seborrheic keratosis [44], [45], [47], [48], [50], [52], [53], [55], [60], [61], [62], [71], [75], [79], [80], [83], and [84] Malignant neoplasms Basal cell carcinoma [38], [39], [41], [42], [54], [56], [57...…”

“…The HAM10000 dataset is a dermoscopic image collected from the Dermatology Department of Vienna Medical University in Austria and the dermatology practice of Cliff Rosendahl in Queensland, Australia [29]. In this study, 18 datasets were collected, including 14 public datasets and 4 self-collected datasets [38], [49], [72], [75]. See Table II for details.…”

“…One-third of the collected literature use the mixture of multiple datasets [38], [42], [43], [46], [47], [49], [51], [52], [53], [58], [73], [76], [80], [85]. [49], [53], [54], [56], [57], [66], [71], [76], [79], [84], and [85] ISIC Archive [30] 23906 7 √ [46], [58], [59], [70], [77], [79], [80], and [81] DermIS [32] --× [46], [51], [58], and [80] DermQuest [33] --× [46], [51], and [80] DermNZ [34] --× [46] and [51] ISIC 2016 [35] 1279 2 √ [47], [52], …”

“…Rahul et al used nonlocal means deoiling method to remove noise [58]. Xiaoyu added noise to the skin disease image to study image noise's influence on skin disease recognition [38].…”

“…Table III shows the details. [47], [50], [51], [52], [53], [58], and [65] Clipping [38], [44], [49], [53], [55], [56], [57], and [75] - [41], [42], [59], and [70] Normalization Data normalization [41], [47], [48], [52], [53], [54], [56], [58], [65], [70], and [75] This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ This article has been accepted for publication in a future issue of this journal, but has not been fully edited.…”