Balamanigandan R
ABSTRACT
Abstract: Lung cancer remains a significant global health challenge, demanding precise and timely diagnostic interventions for improved patient outcomes. This research proposes an innovative approach, the Optimized-ANN (Artificial Neural Network) method, to improve the precision oflung cancer diagnostic through the integration of machine learning techniques. By optimizing the architecture and parameters of the ANN, we aim to achieve superior diagnostic precision, aiding clinicians in early detection and tailored treatment planning. The Optimized-ANN methodology involves a multi-step process, encompassing preprocessing of medical imaging data, Principal component analysis (PCA) for dimensionality reduction and feature extraction, hyperparameter optimization, and construction of a customized ANN. The resulting model is trained and validated using a diverse dataset, with a focus on robustness and generalization to various patient profiles. Our research adds to the corpus of knowledge by providing a thorough and refined method of diagnosing lung cancer. The evaluation Metrics like F1-score, recall, accuracy, and precision providea detailed understanding of the design's performance. Furthermore, cross-validation ensures the reliability of the Optimized-ANN across distinct subsets of the dataset. The anticipated outcomes of this research include heightened diagnostic accuracy, efficient feature representation, and adaptability to diverse imaging conditions. As lung cancer diagnosis relies heavily on medical imaging, the Optimized-ANN Approach holds the potential to significantly impact clinical decision-making, facilitating earlier interventions and ultimately improving patient prognosis. This paper sets the stage for the detailed exploration of the Optimized-ANN Approach, underscoring its potential as a valuable tool in the realm of lung cancer diagnosis and contributing to the broader landscape of machine learning applications in healthcare.
- ) Bansal, Aditya, Timothy R. DeGrado, and Mukesh K. Pandey. 2023. “Positron Emission Tomography Imaging of Cell Trafficking: A Method of Cell Radiolabeling.” Journal of Visualized Experiments: JoVE, no. 200 (October). https://doi.org/10.3791/64117.Google Scholar
Cross Ref
- ) Chen, Chen, Yitao Jiang, Jincao Yao, Min Lai, Yuanzhen Liu, Xianping Jiang, Di Ou, 2023. “Deep Learning to Assist Composition Classification and Thyroid Solid Nodule Diagnosis: A Multicenter Diagnostic Study.” European Radiology, October. https://doi.org/10.1007/s00330-023-10269-z.Google ScholarCross Ref
- ) Demondion, Emilie, Olivier Ernst, Alexandre Louvet, Benjamin Robert, Galit Kafri, Eran Langzam, and Mathilde Vermersch. 2023. “Hepatic Fat Quantification in Dual-Layer Computed Tomography Using a Three-Material Decomposition Algorithm.” European Radiology, November. https://doi.org/10.1007/s00330-023-10382-z.Google ScholarCross Ref
- ) Genseke, Philipp, Christoph Ferdinand Wielenberg, Jens Schreiber, Eva Luecke, Steffen Frese, Thorsten Walles, and Michael Christoph Kreissl. 2023. “Prospective Evaluation of Quantitative F-18-FDG-PET/CT for Pre-Operative Thoracic Lymph Node Staging in Patients with Lung Cancer as a Target for Computer-Aided Diagnosis.” Diagnostics (Basel, Switzerland) 13 (7). https://doi.org/10.3390/diagnostics13071263.Google ScholarCross Ref
- ) Higgins, Kristin, Jessy Deshane, and Fiona Hegi-Johnson. 2023. Factors That Impact the Survival of Non-Small Cell Lung Cancer. Frontiers Media SA.Google Scholar
- ) Mottram, Carl. 2022. Ruppel's Manual of Pulmonary Function Testing - E-Book. Elsevier Health Sciences.Google Scholar
- ) Ozcelik, Neslihan, Mehmet Kıvrak, Abdurrahman Kotan, and İnci Selimoğlu. 2023. “Lung Cancer Detection Based on Computed Tomography Image Using Convolutional Neural Networks.” Technology and Health Care: Official Journal of the European Society for Engineering and Medicine, October. https://doi.org/10.3233/THC-230810.Google ScholarDigital Library
- ) Shields, Allison, Kyle Williams, Mohammad Mahdi Shiraz Bhurwani, Swetadri Vasan Setlur Nagesh, Venkat Keshav Chivukula, Daniel R. Bednarek, Stephen Rudin, Jason Davies, Adnan H. Siddiqui, and Ciprian N. Ionita. 2023. “Enhancing Cerebral Vasculature Analysis with Pathlength-Corrected 2D Angiographic Parametric Imaging: A Feasibility Study.” Medical Physics, October. https://doi.org/10.1002/mp.16808.Google ScholarCross Ref
- ) Shin, Heejun, Taehee Kim, Juhyung Park, Hruthvik Raj, Muhammad Shahid Jabbar, Zeleke Desalegn Abebaw, Jongho Lee, Cong Cung Van, Hyungjin Kim, and Dongmyung Shin. 2023. “Pulmonary Abnormality Screening on Chest X-Rays from Different Machine Specifications: A Generalized AI-Based Image Manipulation Pipeline.” European Radiology Experimental 7 (1): 68.Google ScholarCross Ref
- ) Szpor, Joanna, Karolina Witczak, Monika Storman, Anna Streb-Smoleń, Agnieszka Krzemień, Krzysztof Okoń, Diana Hodorowicz-Zaniewska, and Joanna Streb. 2023. “Breast Carcinoma Grading on Core Needle Biopsy - to Grade or Not to Grade?” Polish Journal of Pathology: Official Journal of the Polish Society of Pathologists 74 (3): 203–10.Google Scholar
- ) Uchino, Junji, Torsten Goldmann, and Hideharu Kimura. 2022. Treatment for Non Small Cell Lung Cancer in Distinct Patient Populations. Frontiers Media SA.Google Scholar
- ) Wang, Yang, Junkai Zhu, Xiaofan Lu, Wenxuan Cheng, Li Xu, Xin Wang, Jian Wang, 2023. “Development and Validation of Radiomics Nomograms for Preoperative Prediction of Characteristics in Non-Small Cell Lung Cancer and Circulating Tumor Cells.” Medicine 102 (44): e35830.Google ScholarCross Ref
- ) Wekking, D., M. Porcu, B. Pellegrino, E. Lai, G. Mura, N. Denaro, L. Saba, A. Musolino, M. Scartozzi, and C. Solinas. 2023. “Multidisciplinary Clinical Guidelines in Proactive Monitoring, Early Diagnosis, and Effective Management of Trastuzumab Deruxtecan (T-DXd)-Induced Interstitial Lung Disease (ILD) in Breast Cancer Patients.” ESMO Open 8 (6): 102043.Google ScholarCross Ref
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