{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T20:03:53Z","timestamp":1773259433127,"version":"3.50.1"},"reference-count":0,"publisher":"SASA Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JOWUA"],"published-print":{"date-parts":[[2025,6,30]]},"abstract":"<jats:p>The death rate for people diagnosed with Lung Cancer (LC) is rather high. Patients' lives may be\nsaved if this illness is detected early and the stage of lung cancer is correctly identified. To determine\nwhether a patient has lung cancer, traditional approaches use manual CT scans. This study presents\na new approach to cancer cell segmentation and classification utilizing a Hybrid Deep Learning\nNeural Network (HDL) as a means of making an accurate and early diagnosis. What makes an HNN\nunique is its combination of the OTSU segmentation model with a Convolutional Neural Network\n(CNN) for feature extraction from CT image datasets and an enhanced LSTM: RNN classification\nmodel for improved classification accuracy. Prioritizing good health and well-being is essential for\nliving a fulfilling and balanced life, enabling individuals to thrive both physically and mentally. The\nproposed method also makes it possible to distinguish between benign and cancerous tumors. We\nconducted a simulation experiment using the IQ-OTH\/NCCD LC dataset and measured outcomes\nusing the various performance metrics. According to the findings, the assessment criteria\nsignificantly reduce the classification time by around 50% while maintaining nearly the same\nclassification impact. Based on the results of the simulations, our solution outperforms the classic\nclassification algorithm in terms of convergence speed and time consumption, all while maintaining\nhigh classification accuracy. The study provides an attractive tool for quick image classification with\ngreat real-time performance.<\/jats:p>","DOI":"10.58346\/jowua.2025.i2.005","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T07:10:46Z","timestamp":1754550646000},"page":"75-94","source":"Crossref","is-referenced-by-count":1,"title":["Hybrid Deep Learning Model Based Lung Cancer Prediction and Classification with OTSU Segmentation Method"],"prefix":"10.58346","volume":"16","author":[{"given":"N. Muthu","family":"Bala","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dr.K.S.","family":"Kannan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"37075","published-online":{"date-parts":[[2025,6,30]]},"container-title":["Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications"],"original-title":[],"deposited":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T07:10:47Z","timestamp":1754550647000},"score":1,"resource":{"primary":{"URL":"https:\/\/jowua.com\/wp-content\/uploads\/2025\/08\/2025.I2.005.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,30]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,6,30]]},"published-print":{"date-parts":[[2025,6,30]]}},"URL":"https:\/\/doi.org\/10.58346\/jowua.2025.i2.005","relation":{},"ISSN":["2093-5374","2093-5382"],"issn-type":[{"value":"2093-5374","type":"print"},{"value":"2093-5382","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,30]]}}}