Diagnosis of Visible Diseases Using CNNs

Sandeep, R; Vishal, K. P.; Shamanth, M. S.; Chethan, K.

doi:10.1007/978-981-19-0976-4_38

Cited by 23 publications

(11 citation statements)

References 8 publications

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“…In comparison, their proposed solution demonstrates the best results by achieving an accuracy of around 78%. Velasco et al (2019) proposed a smartphone-based skin disease identification utilizing MobileNet and reported around 94.4% accuracy in detecting patients with chickenpox symptoms [21]. Roy et al (2019) utilized different segmentation approaches to detect skin diseases such as acne, candidiasis, cellulitis, chickenpox, etc [22].…”

Section: Introductionmentioning

confidence: 99%

Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16

Ahsan¹,

Uddin²,

Farjana³

et al. 2022

Preprint

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While the world is still attempting to recover from the damage caused by the broad spread of COVID-19, the monkeypox virus poses a new threat of becoming a global pandemic. Although the Monkeypox virus itself is not deadly and contagious as COVID-19, still every day, new patients case has been reported from many nations. Therefore, it will be no surprise if the world ever faces another global pandemic due to the lack of proper precautious steps. Recently, Machine learning (ML) has demonstrated huge potential in image-based diagnoses such as cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, a similar application can be adopted to diagnose the Monkeypox related disease as it infected the human skin, which image can be acquired and further used in diagnosing the disease. However, there is no publicly available Monkeypox dataset that can be used for training and experimenting the ML model development. Consequently, there is an immediate need to develop a dataset containing Monkeypox infected patients' images. Considering this opportunity, in this work, we introduce a newly developed "Monkeypox2022" dataset that is publicly available to use and can be obtained from our shared GitHub repository. The dataset is created by collecting images from multiple open-source and online portals that do not impose any restrictions on use, even for commercial purposes, hence giving a safer path to use and disseminate such data when constructing and deploying any type of ML models. Further, we propose and evaluate a modified VGG16 model, which includes two distinct studies: Study One and Two. Our exploratory computational results indicate that our suggested model can identify Monkeypox patients with an accuracy of 97 ± 1.8% (AUC = 97.2) and 88 ± 0.8% ( AUC = 0.867) for Study One and Two, respectively. Additionally, we explain our model's prediction and feature extraction utilizing Local

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Section: Introductionmentioning

confidence: 99%

Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16

Ahsan¹,

Uddin²,

Farjana³

et al. 2022

Preprint

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“…In addition, a nine-way illness partition was carried out in order to place each lesion that was examined into one of the nine categories that were previously described. In their article [ 51 ], Sandeep et al examined the use of DL-based approaches for the detection of various skin lesions. They came up with a CNN to separate skin lesions into the eight different illness categories.…”

Section: Literature Reviewmentioning

confidence: 99%

An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease

et al. 2022

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Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing monkeypox. Such conditions often carry less obvious but no less devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due to the low visual resolution of monkeypox disease images, medical specialists with high-level tools are typically required for a proper diagnosis. The manual diagnosis of monkeypox disease is subjective, time-consuming, and labor-intensive. Therefore, it is necessary to create a computer-aided approach for the automated diagnosis of monkeypox disease. Most research articles on monkeypox disease relied on convolutional neural networks (CNNs) and using classical loss functions, allowing them to pick up discriminative elements in monkeypox images. To enhance this, a novel framework using Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) is proposed to fine-tune the deep CNN layers for classifying monkeypox disease from images. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space and learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework’s efficacy, as it outperforms numerous examples of prior research on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models.

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“…(2022) proposed a low complex CNN to detect skin diseases such as Psoriasis, Melanoma, Lupus, and Chickenpox. They show that using exiting VGGNet; it is possible to detect skin disease 71% accurately using image analysis ( Sandeep, Vishal, Shamanth, & Chethan, 2022 ). In comparison, their proposed solution demonstrates the best results by achieving an accuracy of around 78%.…”

Section: Introductionmentioning

confidence: 99%