Optimizing Computed Tomography Image Reconstruction Parameters for Improved Lung Cancer Diagnosis with Grey Wolf Algorithm
Keywords:
Optimization, Lung Cancer Diagnosis, Image reconstruction, Disease diagnosisAbstract
Worldwide, lung cancer continues to be the most common cause of cancer-related mortality, necessitating ongoing efforts to improve early detection and diagnosis. In this context, computed tomography (CT) imaging is crucial because it makes it possible to see minute pulmonary abnormalities. The quality and diagnostic efficacy of CT scans are substantially impacted by image reconstruction parameters. In order to enhance lung cancer diagnosis, this work proposes a novel method for optimising these parameters utilising the Grey Wolf Algorithm (GWA).The GWA is highly suited for optimisation problems and is inspired by the social dynamics and hunting behaviour of grey wolves. In our study, the kernel selection, filter type, and exposure settings are only a few of the crucial CT image reconstruction factors that we fine-tune using the GWA. The suggested methodology seeks to balance edge preservation with picture noise reduction, ultimately improving the visibility of tiny lung lesions.We carried out extensive trials with a broad dataset of CT images from lung cancer patients to assess the efficacy of our method. Our findings show a considerable improvement in image quality, with less noise and more visible structural details. The better radiologist performance in identifying pulmonary nodules and lesions as a result of the optimised images ultimately increased the precision of lung cancer diagnosis.The GWA-based optimisation strategy also has a number of benefits, such as flexibility to different CT scanner models and robustness in dealing with varying patient demographics. This study emphasises the Grey Wolf Algorithm's potential as a useful tool for enhancing CT image reconstruction parameters and assisting in the early and precise identification of lung cancer, which is essential for improved patient outcomes and lower healthcare costs.
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Jony, M.H.; Johora, F.T.; Khatun, P.; Rana, H.K. Detection of Lung Cancer from CT Scan Images using GLCM and SVM. In Proceedings of the 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, 3–5 May 2019; pp. 1–6.
Zanon, M.; Pacini, G.S.; de Souza, V.V.S.; Marchiori, E.; Meirelles, G.S.P.; Szarf, G.; Torres, F.S.; Hochhegger, B. Early detection of lung cancer using ultra-low-dose computed tomography in coronary CT angiography scans among patients with suspected coronary heart disease. Lung Cancer 2017, 114, 1–5.
Mishra, S.; Thakkar, H.K.; Mallick, P.K.; Tiwari, P.; Alamri, A. A sustainable IoHT based computationally intelligent healthcare monitoring system for lung cancer risk detection. Sustain. Cities Soc. 2021, 72, 103079.
Alakwaa, W.; Mohammad, N.; Amr, B. Lung cancer detection and classification with 3D convolutional neural network (3D-CNN). Int. J. Adv. Comput. Sci. Appl. 2017, 8, 8.
Hatuwal, B.K.; Himal, C.T. Lung cancer detection using convolutional neural network on histopathological images. Int. J. Comput. Trends Technol. 2020, 68, 10, 21–24.
Sasikumar, S.; Renjith, P.N.; Ramesh, K.; Sankaran, K.S. Attention Based Recurrent Neural Network for Lung Cancer Detection. In Proceedings of the 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 7–9 October 2020; pp. 720–724.
Al-Yasriy, H.F.; AL-Husieny, M.S.; Mohsen, F.Y.; Khalil, E.A.; Hassan, Z.S. Diagnosis of lung cancer based on CT scans using CNN. IOP Conf. Ser. Mater. Sci. Eng. 2020, 928, 022035.
Kirubakaran, J.; Venkatesan, G.K.D.P.; Kumar, K.S.; Kumaresan, M.; Annamalai, S. Echo state learned compositional pattern neural networks for the early diagnosis of cancer on the internet of medical things platform. J. Ambient. Intell. Humaniz. Comput. 2020, 12, 3303–3316.
Bahat, B.; Görgel, P. Lung Cancer Diagnosis via Gabor Filters and Convolutional Neural Networks. In Proceedings of the 2021 Innovations in Intelligent Systems and Applications Conference (ASYU), Elazig, Turkey, 6–8 October 2021; pp. 1–6.
Hosseini, H.M.; Monsefi, R.; Shadroo, S. Deep Learning Applications for Lung Cancer Diagnosis: A systematic review.arXiv 2020, arXiv:2201.00227.
Ozdemir, O.; Russell, R.L.; Berlin, A.A. A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans. IEEE Trans. Med. Imaging 2019, 39, 1419–1429.
Capuano, R.; Catini, A.; Paolesse, R.; Di Natale, C. Sensors for Lung Cancer Diagnosis. J. Clin. Med. 2019, 8, 235.
S. Ajani and M. Wanjari, "An Efficient Approach for Clustering Uncertain Data Mining Based on Hash Indexing and Voronoi Clustering," 2013 5th International Conference and Computational Intelligence and Communication Networks, 2013, pp. 486-490, doi: 10.1109/CICN.2013.106.
Khetani, V. ., Gandhi, Y. ., Bhattacharya, S. ., Ajani, S. N. ., & Limkar, S. . (2023). Cross-Domain Analysis of ML and DL: Evaluating their Impact in Diverse Domains. International Journal of Intelligent Systems and Applications in Engineering, 11(7s), 253–262.
Borkar, P., Wankhede, V.A., Mane, D.T. et al. Deep learning and image processing-based early detection of Alzheimer disease in cognitively normal individuals. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08615-w
Ajani, S.N., Mulla, R.A., Limkar, S. et al. DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis. Soft Comput (2023). https://doi.org/10.1007/s00500-023-08613-y
Shimizu, R.; Yanagawa, S.; Monde, Y.; Yamagishi, H.; Hamada, M.; Shimizu, T.; Kuroda, T. Deep learning appli-cation trial to lung cancer diagnosis for medical sensor systems. In Proceedings of the 2016 International SoC Design Conference (ISOCC), Jeju, Korea, 23–26 October 2016; pp. 191–192.
Mishra, S.; Chaudhary, N.K.; Asthana, P.; Kumar, A. Deep 3d convolutional neural network for automated lung cancer diagnosis. In Computing and Network Sustainability: Proceedings of IRSCNS 2018; Springer: Singapore, 2019; pp. 157–165.
Zhang, Y.; Simoff, M.J.; Ost, D.; Wagner, O.J.; Lavin, J.; Nauman, B.; Hsieh, M.-C.; Wu, X.-C.; Pettiford, B.; Shi, L. Understanding the patient journey to diagnosis of lung cancer. BMC Cancer 2021, 21, 402.
Alsammed, S.M.Z.A. Implementation of Lung Cancer Diagnosis based on DNN in Healthcare System. Webology 2021, 18, 798–812.
Pradhan, K.; Chawla, P. Medical Internet of things using machine learning algorithms for lung cancer detection. J. Manag. Anal. 2020, 7, 591–623.
Valluru, D.; Jeya, I.J.S. IoT with cloud based lung cancer diagnosis model using optimal support vector machine. Heal. Care Manag. Sci. 2019, 23, 670–679.
Souza, L.F.D.F.; Silva, I.C.L.; Marques, A.G.; Silva, F.H.D.S.; Nunes, V.X.; Hassan, M.M.; Filho, P.P.R. Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation. Sensors 2020, 20, 6711.
Chakravarthy, S.; Rajaguru, H. Lung Cancer Detection using Probabilistic Neural Network with modified Crow-Search Algorithm. Asian Pac. J. Cancer Prev. 2019, 20, 2159–2166.
Palani, D.; Venkatalakshmi, K. An IoT Based Predictive Modelling for Predicting Lung Cancer Using Fuzzy Cluster Based Segmentation and Classification. J. Med. Syst. 2018, 43, 21.
Faruqui, N.; Abu Yousuf, M.; Whaiduzzaman; Azad, A.; Barrosean, A.; Moni, M.A. LungNet: A hybrid deep-CNN model for lung cancer diagnosis using CT and wearable sensor-based medical IoT data. Comput. Biol. Med. 2021, 139, 104961.
Ozsandikcioglu, U.; Atasoy, A.; Yapici, S. Hybrid Sensor Based E-Nose For Lung Cancer Diagnosis. In Proceedings of the 2018 IEEE International Symposium on Medical Measurements and Applications (MeMeA), Rome, Italy, 11–13 2018; pp. 1–5.
Kiran, S.V.; Kaur, I.; Thangaraj, K.; Saveetha, V.; Grace, R.K.; Arulkumar, N. Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images. Int. J. Image Graph. 2021, 1, 2240002.
Shan, R.; Rezaei, T. Lung Cancer Diagnosis Based on an ANN Optimized by Improved TEO Algorithm. Comput. Intell. Neurosci. 2021, 2021, 6078524.
Robert Roberts, Daniel Taylor, Juan Herrera, Juan Castro, Mette Christensen. Integrating Virtual Reality and Machine Learning in Education. Kuwait Journal of Machine Learning, 2(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/175
Salman Al-Nuaimi, M. A. ., & Abdu Ibrahim, A. . (2023). Analyzing and Detecting the De-Authentication Attack by Creating an Automated Scanner using Scapy. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 131–137. https://doi.org/10.17762/ijritcc.v11i2.6137
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