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Optimized deep learning model for early detection and classification of lung cancer on CT images.

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Recently, researchers have shown an increased interest in the early diagnosis and detection of lung cancer using the characteristics of computed tomography (CT) images. The accurate classification of lung cancer assists the physician to know the targeted treatment, reducing mortality, and as a result, supporting human survival. Several studies have been carried out on lung cancer detection using a convolutional neural network (CNN) models. However, it still remains a challenge to improve the model’s performance. Moreover, CNN models have some limitations that affect their performance, including choosing the optimal architecture, selecting suitable model parameters, and picking the best parameter values for weights and bias. To address the problem of selecting the best combination of weights and bias needed for the classification of lung cancer in CT images, this study proposes a hybrid of Ebola optimization search algorithm (EOSA) and the CNN model. We proposed a hybrid deep learning model with preprocessing features for lung cancer classification using publicly accessible Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) dataset. The proposed EOSA-CNN hybrid model was trained using 80% of the cases to obtain the optimal configuration, while the remaining 20% was applied for validation. Also, we compared the proposed model with similar five hybrid algorithms and the traditional CNN. The results indicated that EOSA-CNN scored 0.9321 classification accuracy. Furthermore, the result showed that EOSA-CNN achieved a specificity of 0.7941, 0.97951, 0.9328, and sensitivity of 0.9038, 0.13333, 0.9071 for normal, benign, and malignant cases, respectively. This confirmed that the hybrid algorithm provides a good solution for the classification of lung cancer.


Masters Degree. University of KwaZulu-Natal, Pietermaritzburg.