Deep Learning Based Image Classification for Remote Medical Diagnosis

Shihadeh, Juliana; Ansari, Anaam; Ozunfunmi, Tokunbo

doi:10.1109/ghtc.2018.8601558

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…Table 1 summarizes the characteristics of the selected studies. Figure 3 shows the number of papers published per year: 4 of 53 studies (7.6%) were published before 2016 [ 10 - 13 ], 26 studies (49.1%) were published in 2016, 2017, and 2018 [ 14 - 39 ], and 23 studies (43.4%) were published in 2019 and 2020 [ 40 - 62 ]. Although our selection criteria included papers published between 2009 and July 2020, the oldest published paper included after the full-text review was published in 2011.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Skin Cancer Detection: Scoping Review

Takiddin¹,

Schneider²,

Yang³

et al. 2021

J Med Internet Res

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Background Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tools are being used, including shallow and deep machine learning–based methodologies that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. Objective The aim of this study was to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examined the reliability of the selected papers by studying the correlation between the data set size and the number of diagnostic classes with the performance metrics used to evaluate the models. Methods We conducted a systematic search for papers using Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery Digital Library (ACM DL), and Ovid MEDLINE databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. The studies included in this scoping review had to fulfill several selection criteria: being specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were independently conducted by two reviewers. Extracted data were narratively synthesized, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. Results We retrieved 906 papers from the 3 databases, of which 53 were eligible for this review. Shallow AI-based techniques were used in 14 studies, and deep AI-based techniques were used in 39 studies. The studies used up to 11 evaluation metrics to assess the proposed models, where 39 studies used accuracy as the primary evaluation metric. Overall, studies that used smaller data sets reported higher accuracy. Conclusions This paper examined multiple AI-based skin cancer detection models. However, a direct comparison between methods was hindered by the varied use of different evaluation metrics and image types. Performance scores were affected by factors such as data set size, number of diagnostic classes, and techniques. Hence, the reliability of shallow and deep models with higher accuracy scores was questionable since they were trained and tested on relatively small data sets of a few diagnostic classes.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Skin Cancer Detection: Scoping Review

Takiddin¹,

Schneider²,

Yang³

et al. 2021

J Med Internet Res

View full text Add to dashboard Cite

show abstract

“…Targeting the classification of skin cancer images as benign or malignant, an application based on CNN via AlexNet was developed. This CNN based application permits easy access for medical diagnosis and is targeted for the use of underdeveloped countries [18]. Any novel method based on deep learning for the extraction of lesion region requires preprocessing to reduce artefacts; the input image is then given to the deep CNN, where it produces a segmentation mask of the lesion region [19].…”

Section: Theory and Methodsmentioning

confidence: 99%

Convolutional Neural Network and Fuzzy Logic-based Hybrid Melanoma Diagnosis System

Yalcinkaya

Erbas

2021

ELEKTRON ELEKTROTECH

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Studies on the detection of early stage melanoma have recently gained significant interest. Computer aided diagnosis systems based on neural networks, machine learning, convolutional neural networks (CNNs), and deep learning help early stage detection considerably. The colour and shapes of the images created by the pixels are crucial for the CNNs, as the pixels and associated pictures are interrelated just as a person’s fingerprint is unique. By observing this relationship, the pixel values of each picture with its neighborhoods were determined by a fuzzy logic-based system and a unique fingerprint matrix named Fuzzy Correlation Map (FCov-Map) was produced. The fuzzy logic system has four inputs and one output. The advantage of CNNs trained with fuzzy covariance maps is to eliminate both the limited availability of medical grade training data and the need for extensive image preprocessing. The fuzzy logic output is fed to the pretrained AlexNet CNN algorithm. To deliver a reliable result, a deep CNN needs a large amount of data to process. However, to obtain and use the required sufficient data for diseases is not cost- and time-effective. Therefore, the suggested fuzzy logic-based fuzzy correlation map is tackling this issue to solve the limitedness of training CNN data set.

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“…In [8], J. Shihadeh et al propose a skin cancer image classification based on AlexNet [2] and GoogLeNet [19] for remote medical diagnosis. The application developed on a light computer node, Nvidia Jetson TX2, achieved 74.57% accuracy.…”

Section: Medical Image Classificationmentioning

confidence: 99%

Deep and Lightweight Neural Network for Histopathological Image Classification

Kim

Yoon

2022

JMM

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Breast cancer is a fatal disease affecting women, and early detection and proper treatment are crucial. Classifying medical images correctly is the first and most important step in the cancer diagnosis stage. Deep learning-based classification methods in various domains demonstrate advances in accuracy. However, as deep learning improves, the layers of neural networks get deeper, raising challenges, such as overfitting and gradient vanishing. For instance, a medical image is simpler than an ordinary one, making it vulnerable to overfitting issues. We present breast histopathological classification methods with two deep neural networks, Xception and LightXception with aid of voting schemes over split images. Most deep neural networks classify thousands classes of images, but the breast histopathological image classes are far fewer than those of other image classification tasks. Because the BreakHis dataset is relatively simpler than typical image datasets, such as ImageNet, applying the conventional highly deep neural networks may suffer from the aforementioned overfitting or gradient vanishing problems. Additionally, highly deep neural networks require more resources, leading to high computational costs. Consequently, we propose a new network; LightXception by cutting off layers at the bottom of the Xception network and reducing the number of channels of convolution filters. LightXception has only about 35% of parameters compared to those of the original Xception network with minimal expense on performance. Based on images with 100X magnification factor, the performance comparisons for Xception vs. LightXception are 97.42% vs. 97.31% on classification accuracy, 97.42% vs. 97.42% on recall, and 99.26% vs. 98.67% of precision.

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Deep Learning Based Image Classification for Remote Medical Diagnosis

Cited by 12 publications

References 8 publications

Artificial Intelligence for Skin Cancer Detection: Scoping Review

Artificial Intelligence for Skin Cancer Detection: Scoping Review

Convolutional Neural Network and Fuzzy Logic-based Hybrid Melanoma Diagnosis System

Deep and Lightweight Neural Network for Histopathological Image Classification

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