Abstract
In this paper, an efficient classification model for classifying COVID-19 based on X-ray and computed tomography (CT) images were introduced. Medical dataset images were acquired from available open sources. Chest X-ray and CT images are considered critical diagnostic tools, especially in the scarcity of reverse transcription-polymerase chain reaction (RT-PCR) test kits. Routinely, for the detection of pneumonia, doctors frequently use X-rays of the chest to analyze the infection quickly. Deep learning is used successfully as a tool for machine learning, where a neural network is capable of automatically learning features. Among deep learning techniques, deep convolutional networks are actively used for medical image analysis. The proposed approach modified the well-known convolutional neural network, named AlexNet, to reduce the total computations and keep the model's accuracy. The proposed LightWeight Deep Convolutional Neural Network (LWCOV) is finished with three layers of the fully connected 512 SoftMax. Tests have proven satisfactory classification results compared to some recent models, and it is superior in calculation speed, which saves resource consumption.
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El-Baz, A., Saber, W., Rizk, R.Y. (2021). LWCOV: LightWeight Deep Convolutional Neural Network for COVID-19 Detection. In: Hassanien, AE., Chang, KC., Mincong, T. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2021. Advances in Intelligent Systems and Computing, vol 1339. Springer, Cham. https://doi.org/10.1007/978-3-030-69717-4_2
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