Affiliations: [a] Computer Science and Engineering, G. H. Raisoni University, Amravati, Maharashtra, India | [b] Head of the Department, CSE (Cyber Security and Data Science) GHRCM, Pune associated with G. H. Raisoni University, Amravati, Maharashtra, India | [c] Information Technology, Sandip Foundation’s, Sandip Institute of Technology and Research Centre, Nashik, Maharashtra, India
Correspondence:
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Corresponding author: Ajit Narendra Gedam, Research Scholar, Computer Science and Engineering, G. H. Raisoni University, Amravati, Maharashtra 444701, India. Email: [email protected].
Abstract: PROBLEM: Lung cancer is a dangerous and deadly disease with high mortality and reduced survival rates. However, the lung nodule diagnosis performance is limited by its heterogeneity in terms of texture, shape, and intensity. Furthermore, the high degree of resemblance between the lung nodules and the tissues that surround the lung nodules makes the building of a reliable detection model more difficult. Moreover, there are several methods for diagnosing and grading lung nodules; still, the accuracy of detection with the variations in intensity is a challenging task. AIM & METHODS: For the detection of lung nodules and grading, this research proposes an Eyrie Flock Optimization-based Deep Convolutional Neural Network (Eyrie Flock-DeepCNN). The proposed Eyrie Flock Optimization integrates the fishing characteristics of Eyrie’s and the flocking characteristics of Tusker to accelerate the convergence speed which inturns enhance the training process and improve the generalization performance of the DeepCNN model. In the Eyrie Flock optimization, two optimal issues are considered: (i) segmenting the lung nodule and (ii) fine-tuning hyperparameters of Deep CNN. RESULTS: The capability of the newly developed method is evaluated by the terms of Specificity, Sensitivity, and Accuracy, attaining 98.96%, 95.21%, and 94.12%, respectively. CONCLUSION: Efficiently utilized the Deep CNN along with the help of the Eyrie Flock optimization algorithm which enhances the efficiency of the classifier and convergence of the model.
