A Hybrid Optimization Approach for Pulmonary Nodules Segmentation and Classification using Deep CNN

Ajit Narendra Gedam; Aniruddha S. Rumale

doi:10.4108/eetpht.10.4855

Authors

Ajit Narendra Gedam G. H. Raisoni University
Aniruddha S. Rumale G. H. Raisoni University

DOI:

https://doi.org/10.4108/eetpht.10.4855

Keywords:

Nodule Detection, Optimization, Segmentation, Computed Tomography, Computer-Aided Diagnosis, 3D-CNN, Deep CNN

Abstract

Lung Cancer, due to a lower survival rate, is a deadly disease as compared to other cancers. The prior determination of the lung cancer tends to increase the survival rate. Though there are numerous lung cancer detection techniques, they are all insufficient to detect accurate cancer due to variations in the intensity of the CT scan image. For more accuracy in segmentation of CT images, the proposed Elephant-Based Bald Eagle Optimization (EBEO) algorithm is used. This proposed research concentrates on developing a lung nodule detection technique based on Deep learning. To obtain an effective result, the segmentation process will be carried out using the proposed algorithm. Further, the proposed algorithm will be utilized to tune the hyper parameter of the deep learning classifier to increase detection accuracy. It is expected that the proposed state-of-art method will exceed all conventional methods in terms of detection accuracy due to the effectiveness of the proposed algorithm. This survey will be helpful for the healthcare research communities with sufficient knowledge to understand the concepts of the EBEO algorithm and the Deep Convolutional Neural Network for improving the overall human healthcare system.

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