LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images.

According to the World Health Organization, an estimate of more than five million infections and 355,000 deaths have been recorded worldwide since the emergence of the coronavirus disease (COVID-19). Various researchers have developed interesting and effective deep learning frameworks to tackle this disease. However, poor feature extraction from the Chest X-ray images and the high computational cost of the available models impose difficulties to an accurate and fast Covid-19 detection framework. Thus, the major purpose of this study is to offer an accurate and efficient approach for extracting COVID-19 features from chest X-rays that is also less computationally expensive than earlier research. To achieve the specified goal, we explored the Inception V3 deep artificial neural network. This study proposed LCSB-Inception; a two-path (L and AB channel) Inception V3 network along the first three convolutional layers. The RGB input image is first transformed to CIE LAB coordinates (L channel which is aimed at learning the textural and edge features of the Chest X-Ray and AB channel which is aimed at learning the color variations of the Chest X-ray images). The L achromatic channel and the AB channels filters are set to 50%L-50%AB. This method saves between one-third and one-half of the parameters in the divided branches. We further introduced a global second-order pooling at the last two convolutional blocks for more robust image feature extraction against the conventional max-pooling. The detection accuracy of the LCSB-Inception is further improved by employing the Contrast Limited Adaptive Histogram Equalization (CLAHE) image enhancement technique on the input image before feeding them to the network. The proposed LCSB-Inception network is experimented on using two loss functions (Categorically smooth loss and categorically Cross-entropy) and two learning rates whereas Accuracy, Precision, Sensitivity, Specificity F1-Score, and AUC Score were used for evaluation via the chestX-ray-15k (Data_1) and COVID-19 Radiography dataset (Data_2). The proposed models produced an acceptable outcome with an accuracy of 0.97867 (Data_1) and 0.98199 (Data_2) according to the experimental findings. In terms of COVID-19 identification, the suggested models outperform conventional deep learning models and other state-of-the-art techniques presented in the literature based on the results.