An EfficientNet-based modified sigmoid transform for enhancing dermatological macro-images of melanoma and nevi skin lesions

COVID-19 is a positive-sense single-stranded RNA virus caused by a strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several noteworthy variants of SARS-CoV-2 were declared by WHO as Alpha, Beta, Gamma, Delta, and Omicron. Till 13/Dec/2022, it has caused 6.65 million death tolls, and over 649 million confirmed positive cases. Based on the convolutional neural network (CNN), this study first proposes a ten-layer CNN as the backbone model. Then, the exponential linear unit (ELU) is introduced to replace ReLU, and the traditional convolutional block is now transformed into conv-ELU. Finally, an ELU-based CNN (ELUCNN) model is proposed for COVID-19 diagnosis. Besides, the MDA strategy is used to enhance the size of the training set. We develop a mobile app integrating ELUCNN, and this web app is run on a client–server modeled structure. Ten runs of the tenfold cross-validation experiment show our model yields a sensitivity of $$94.41\pm 0.98$$ 94.41 ± 0.98 , a specificity of $$94.84\pm 1.21$$ 94.84 ± 1.21 , an accuracy of $$94.62\pm 0.96$$ 94.62 ± 0.96 , and an F1 score of $$94.61\pm 0.95$$ 94.61 ± 0.95 . The ELUCNN model and mobile app are effective in COVID-19 diagnosis and give better results than 14 state-of-the-art COVID-19 diagnosis models concerning accuracy.

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“…The output of Sigmoid (Venugopal et al 2022 ) is in the range of . In certain situations, the range is anticipated.…”

Section: Proposed Elucnnmentioning

confidence: 99%

ELUCNN for explainable COVID-19 diagnosis

et al. 2023

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“…The EfficientNet B0 architecture wasn't forged by engineers but by the neural network itself. This model was devised using a multi-objective neural architecture search that optimizes both accuracy and floating-point functions [18]. Taking B0 as a baseline prototype, a robust family of EfficientNets from B1 to B7 perpetrated state-of-the-art precision on ImageNet.…”

Section: Proposed Modelmentioning

confidence: 99%

Effective machine learning-based skin disease diagnosis using PyTorch

Kumar,

Wang,

Mukundan

et al. 2023

J. Phys.: Conf. Ser.

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The application of machine learning in medical diagnosis has become a trend in research. Skin infection is one of the most seen diseases and one of the world’s most infectious diseases, influencing people of all ages. The reason for the explicit attention of the researchers in skin detection is due to the reason that skin disease is more visible compared to any other disease. In the past, varied methods have been proposed, which have rendered remarkable results. However, the presently functional models are trained on specific kinds of diseases and are limited to 4 to 5 classes, which is inefficient in detecting a large set of diseases. The paper offers a weightless model for detecting 23 different kinds of skin diseases. The model is trained on the PyTorch backend, which gives the flexibility of developing an algorithm. The model attained 96.37% accuracy on training data, and 87.75% accuracy on test data, which is expected to improve as the size of the dataset is increased.

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“…EfficientNet (Tan & Le, 2019) is a novel convolutional neural network architecture for analyzing images and is preferred as a prediction model in various disease diagnostic research for humans (Venugopal, Joseph, Das, & Nath, 2022) (Nayak, Padhy, Mallick, Zymbler, & Kumar, 2022) (Wang, Liu, Xie, Yang, & Zhou, 2021) (Ravi, Acharya, & Alazab, 2022) (Zhu et al, 2022) (Marques, Ferreras, & de la Torre-Diez, 2022) and plants (Hanh, Van Manh, & Nguyen, 2022) (Farman et al, 2022) (Atila, Uçar, Akyol, & Uçar, 2021) (Li, Liu, Li, & Liu, 2022). EfficientNet's popularity among deep learning researchers is due to its high classification and prediction performances.…”

Section: Literature Reviewmentioning

confidence: 99%

Diagnosing Diseases From Fingernail Images

CAN

Işık

2022

Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi

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This paper investigates how people's finger and nail appearance helps diagnose various diseases, such as Darier's disease, Muehrcke's lines, alopecia areata, beau's lines, bluish nails, and clubbing, by image processing and deep learning techniques. We used a public dataset consisting of 17 different classes with 655 samples. We divided the dataset into three folds based on a widely used rule, the 0.7:0.2:0.1, for training, validation, and testing purposes. We tested the EfficientNet-B2 model for performance evaluation purposes by using Noisy-Student weights by setting the batch size and epochs as 32 and 1000. The model achieves a 72% accuracy score and 91% AUC score for test samples to detect fingernail diseases. The empirical findings in this study provide a new understanding that the EfficientNet-B2 model can categorize nail disease types through numerous classes.

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An EfficientNet-based modified sigmoid transform for enhancing dermatological macro-images of melanoma and nevi skin lesions

Cited by 29 publications

References 55 publications

ELUCNN for explainable COVID-19 diagnosis

ELUCNN for explainable COVID-19 diagnosis

Effective machine learning-based skin disease diagnosis using PyTorch

Diagnosing Diseases From Fingernail Images

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