DePicT Melanoma Deep-CLASS: a deep convolutional neural networks approach to classify skin lesion images

Abstract

Background: Melanoma results in the vast majority of skin cancer deaths during the last decades, even though this disease accounts for only one percent of all skin cancers' instances. The survival rates of melanoma from early to terminal stages is more than fifty percent. Therefore, having the right information at the right time by early detection with monitoring skin lesions to find potential problems is essential to surviving this type of cancer. Results: An approach to classify skin lesions using deep learning for early detection of melanoma in a case-based reasoning (CBR) system is proposed. This approach has been employed for retrieving new input images from the case base of the proposed system DePicT Melanoma Deep-CLASS to support users with more accurate recommendations relevant to their requested problem (e.g., image of affected area). The efficiency of our system has been verified by utilizing the ISIC Archive dataset in analysis of skin lesion classification as a benign and malignant melanoma. The kernel of DePicT Melanoma Deep-CLASS is built upon a convolutional neural network (CNN) composed of sixteen layers (excluding input and ouput layers), which can be recursively trained and learned. Our approach depicts an improved performance and accuracy in testing on the ISIC Archive dataset. Conclusions: Our methodology derived from a deep CNN, generates case representations for our case base to use in the retrieval process. Integration of this approach to DePicT Melanoma CLASS, significantly improving the efficiency of its image classification and the quality of the recommendation part of the system. The proposed method has been tested and validated on 1796 dermoscopy images. Analyzed results indicate that it is efficient on malignancy detection. BackgroundAlthough case-based reasoning (CBR) has been applied in a number of medical systems, only a few systems have been developed for melanoma. The estimated 5-year survival rate for patients whose melanoma is detected early is about 99% [1] in the USA and around 93% [2] in Germany. The survival rate falls to 63% when the disease reaches the lymph nodes and 20% when the disease metastasizes to distant organs [1]; therefore, having the right information

Deep Learning in Skin Disease Image Recognition: A Review

“…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], …”

“…See Table IV for details. [44], [45], [48], [50], [52], [56], [60], [64], [65], [72], and [81] Image [64] Clipping [60] and [81] Generation of simulation data…”

“…Twenty-seven pieces of literature used a single model in the literature, among which 10 papers ( [38], [38], [43], [57], [59], [64], [73], [75], [76], and [77]) has no model improvement, and the other 15 articles have optimized the model. The optimization methods mainly include: modifying the fully connected layer (five articles: [41], [44], [61], [74], and [80]), using a machine learning classifier (two articles: [65] and [81]), applying (1 article: [51]) the model to the mobile application, introducing attention mechanism (two articles: [50] and [55]), self-building network model (two articles: [58] and [70]), and multilevel classification (three articles: [46], [56], and [78]). Kawahara et al [41] and Yuexiang et al [44] formed a fully CNN based on CNNs and removed the fully connected layers.…”

“…A reasoning (CBR) system is proposed. [81] Deep learning full resolution convolutional network is used. Then, a CNN classifier (i.e., Inception-v3, ResNet-50, Inception-ResNet-v2, and DenseNet-201) is applied on the segmented skin lesions for classification.…”

Main findings and advances in bioinformatics and biomedical engineering- IWBBIO 2018

“…Following, the paper by Sara Nasiri et al [10] address the challenge of discerning between benign and tumoral skin lesions. This paper was focused on designing a powerful diagnosis tool based on deep learning thanks to the use of convolutional neural networks.…”