Pulmonary Diffuse Airspace Opacities Diagnosis from Chest X-Ray Images Using Deep Convolutional Neural Networks Fine-Tuned by Whale Optimizer

Abstract

The early diagnosis and the accurate separation of COVID-19 from non-COVID-19 cases based on pulmonary diffuse airspace opacities is one of the challenges facing researchers. Recently, researchers try to exploit the Deep Learning (DL) method’s capability to assist clinicians and radiologists in diagnosing positive COVID-19 cases from chest X-ray images. In this approach, DL models, especially Deep Convolutional Neural Networks (DCNN), propose real-time, automated effective models to detect COVID-19 cases. However, conventional DCNNs usually use Gradient Descent-based approaches for training fully connected layers. Although GD-based Training (GBT) methods are easy to implement and fast in the process, they demand numerous manual parameter tuning to make them optimal. Besides, the GBT’s procedure is inherently sequential, thereby parallelizing them with Graphics Processing Units is very difficult. Therefore, for the sake of having a real-time COVID-19 detector with parallel implementation capability, this paper proposes the use of the Whale Optimization Algorithm for training fully connected layers. The designed detector is then benchmarked on a verified dataset called COVID-Xray-5k, and the results are verified by a comparative study with classic DCNN, DUICM, and Matched Subspace classifier with Adaptive Dictionaries. The results show that the proposed model with an average accuracy of 99.06% provides 1.87% better performance than the best comparison model. The paper also considers the concept of Class Activation Map to detect the regions potentially infected by the virus. This was found to correlate with clinical results, as confirmed by experts. Although results are auspicious, further investigation is needed on a larger dataset of COVID-19 images to have a more comprehensive evaluation of accuracy rates.

Artificial Intelligence-Enabled Electrocardiography Predicts Left Ventricular Dysfunction and Future Cardiovascular Outcomes: A Retrospective Analysis

“…Third, novel optimization techniques proposed recently were not applied in this study, such as the Whale Optimizer or chimp optimization algorithm. These optimizers could provide better performance and increase the reliability of the network while maintaining its capability [ 15 , 16 , 60 ]. Finally, the ECG-ECHO pairs were not simultaneously acquired.…”

“…In cardiology, Shah et al used a machine learning model to recognize cardiac arrest risk and survival probability [ 14 ]. During the COVID-19 pandemic, the real-time diagnosis of COVID-19 was important and was assisted with DLM-based chest X-ray images with an accuracy of 99% [ 15 , 16 , 17 , 18 ].…”

Deep Transfer Learning for the Multilabel Classification of Chest X-ray Images

“…Alternatively, new architectures—such as EfficientNet [ 28 ] and CoAtNet [ 29 ], which have shown a high performance in challenging computer vision tasks—could be used for analysis. Moreover, to enhance the efficiency of model parameter estimation, Adam may be replaced with recent optimizers such as Chimp [ 9 ] and Whale [ 11 ], and biogeography-based optimization can be applied to automatically finetune model hyperparameters [ 12 ]. Second, CXR images can be taken in posteroanterior, anteroposterior, and lateral views.…”

“…For example, Hu et al [ 9 ] and Wu et al [ 10 ] used an extreme learning machine (ELM) to replace the conventional fully connected layer in a deep CNN for real-time analysis and applied a Chimp optimization algorithm or sine-cosine algorithm to ameliorate the ELM’s ill-conditioning and nonoptimal problems. Wang et al [ 11 ] trained CNN models by using the whale optimization algorithm, which can resolve difficulties related to requiring a considerable amount of manual parameter tuning and parallelizing the training process of traditional gradient descent-based approaches. Khishe et al [ 12 ] proposed an efficient biogeography-based optimization approach for automatically finetuning model hyperparameters (e.g., number of output channels, convolution kernel size, layer type, training algorithm’s learning rate, epoch, and batch size), which are typically selected manually.…”

BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images

“…Optimization algorithms have been proven to be effective tools for selecting optimal features as well as optimizing hyperparameters of ML models in several applications [ 48 ]. Recent methods used for CNN structure optimization in the medical field include the hybrid sine–cosine algorithm [ 49 ], chimp optimization algorithm [ 50 ], and Whale Optimization [ 51 ]. Some recent studies used several optimization approaches to select optimal features for leukemia classification problems [ 52 , 53 , 54 , 55 , 56 ].…”