Conference proceeding
Classification and Detection of Cyanosis Images on Lightly and Darkly Pigmented Individual Human Skins using a Fine-Tuned MobileNet Architecture
2023 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), pp 1-5
03 Aug 2023
Abstract
The classification and detection of cyanosis using in-vivo and in-silico image processing approaches are intriguing and very special. In this study, a peripheral and central cyanosis image classification approach, using lightweight-deep learning Convolutional Neural Networks (CNNs), referred to as pre-trained MobileNet architecture, was introduced. This modified MobileNet model was assessed using the sanctioned dataset of 1300-image collected from multiple cyanosis published datasets. The augmentation technique was applied on the training dataset to enrich the productivity. Emphatic results, validation-accuracy and accuracies on the training and test datasets of 95% and 97%, respectively; were obtained as compared to the validation-accuracy of 79% and 82% of the Simple Convolutional Neural Networks (SCNNs) and Fine-tuned VGG16 models attained from prior stud.
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4 citations in Scopus
Details
- Title
- Classification and Detection of Cyanosis Images on Lightly and Darkly Pigmented Individual Human Skins using a Fine-Tuned MobileNet Architecture
- Creators
- Lukoki Mpova - University of JohannesburgThokozani Shongwe - University of JohannesburgAli Hasan - University of Johannesburg
- Publication Details
- 2023 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD), pp 1-5
- Publisher
- IEEE
- Number of pages
- 5
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Engineering Leadership and Society/Engineering Technology
- Scopus ID
- 2-s2.0-85171989313
- Other Identifier
- 991022004633804721