Abstract

Lung cancer is one of the deadliest types of cancer and has affected thousands of individuals in recent years. Patients have a very low chance of survival if the disease is not diagnosed at an early stage. Early detection can help save the patient’s life and enable simpler, more effective treatment. To facilitate timely diagnosis and recovery, an automated lung cancer detection system is required, and artificial intelligence (AI) can play a key role in addressing this need. In this work, an intelligent system based on a combination of a transfer learning model (ResNet50) and a deep classifier was proposed. The model was trained on the “IQ-OTH/NCCD” dataset and achieved a testing accuracy of up to 98.18%. For practical use, medical images can be submitted through a user interface implemented on the Telegram messaging platform, where the model processes the input and returns classification results in real time. Since the model was trained on a specific dataset, the experimental results demonstrate that it is effective in correctly classifying full lung images from that database.

Keywords

Lung cancer, Medical image classification, Deep learning, Transfer learning, ResNet50

Cite this article

Abbas ZK, Mutashar MR, Sadiq FM. Telegram chatbot for lung cancer diagnosis based on an adaptive combination algorithm using a deep learning model. International Journal of Advanced Computer Research. 2026;16(75):10-22. DOI : 10.19101/IJACR.2025.1570012

References

[1] Sembiring YE, Effendi WI, Dillon JJ, Soebroto H, Winarno DJ, Puruhito P, et al. Lung cancer: a literature review. Jurnal Respirasi. 2023; 9(3):246-51.

[2] Cersosimo RJ. Lung cancer: a review. American Journal of Health-System Pharmacy. 2002; 59(7):611-42.

[3] Collins LG, Haines C, Perkel R, Enck RE. Lung cancer: diagnosis and management. American Family Physician. 2007; 75(1):56-63.

[4] Huang D, Li Z, Jiang T, Yang C, Li N. Artificial intelligence in lung cancer: current applications, future perspectives, and challenges. Frontiers in Oncology. 2024; 14:1-19.

[5] Guo J, Wang C, Xu X, Shao J, Yang L, Gan Y, et al. DeepLN: an artificial intelligence-based automated system for lung cancer screening. Annals of Translational Medicine. 2020; 8(18):1-12.

[6] Gao Q, Yang L, Lu M, Jin R, Ye H, Ma T. The artificial intelligence and machine learning in lung cancer immunotherapy. Journal of Hematology & Oncology. 2023; 16(1):1-18.

[7] Wang L. Deep learning techniques to diagnose lung cancer. Cancers. 2022; 14(22):1-24.

[8] Kareem HF, AL-husieny MS, Mohsen FY, Khalil EA, Hassan ZS. Evaluation of SVM performance in the detection of lung cancer in marked CT scan dataset. Indonesian Journal of Electrical Engineering and Computer Science. 2021; 21(3):1731-8.

[9] Al-yasriy HF, Al-husieny MS, Mohsen FY, Khalil EA, Hassan ZS. Diagnosis of lung cancer based on CT scans using CNN. In IOP conference series: materials science and engineering 2020 (pp. 1-9). IOP Publishing.

[10] Pacurari AC, Bhattarai S, Muhammad A, Avram C, Mederle AO, Rosca O, et al. Diagnostic accuracy of machine learning AI architectures in detection and classification of lung cancer: a systematic review. Diagnostics. 2023; 13(13):1-125.

[11] Mamun M, Mahmud MI, Meherin M, Abdelgawad A. Lcdctcnn: lung cancer diagnosis of CT scan images using CNN based model. In 10th international conference on signal processing and integrated networks (SPIN) 2023 (pp. 205-12). IEEE.

[12] Zheng X, He B, Hu Y, Ren M, Chen Z, Zhang Z, et al. Diagnostic accuracy of deep learning and radiomics in lung cancer staging: a systematic review and meta-analysis. Frontiers in Public Health. 2022; 10:1-10.

[13] Gandhi Z, Gurram P, Amgai B, Lekkala SP, Lokhandwala A, Manne S, et al. Artificial intelligence and lung cancer: impact on improving patient outcomes. Cancers. 2023; 15(21):1-16.

[14] Catherine AT, Towfek SK, Abdelhamid AA. An overview of the evolution and impact of chatbots in modern healthcare services. Mesopotamian Journal of Artificial Intelligence in Healthcare. 2023; 2023:71-5.

[15] Al-huseiny MS, Sajit AS. Transfer learning with GoogLeNet for detection of lung cancer. Indonesian Journal of Electrical Engineering and Computer Science. 2021; 22(2):1078-86.

[16] Nasser IM, Abu-naser SS. Lung cancer detection using artificial neural network. International Journal of Engineering and Information Systems (IJEAIS). 2019; 3(3):17-23.

[17] Mohamed TI, Oyelade ON, Ezugwu AE. Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm. Plos One. 2023; 18(8):1-33.

[18] Alheeti KM, Al-shouka TT, Majeed SH, Ahmed AA. Lung cancer detection using machine learning and deep learning models. In 21st international multi-conference on systems, signals & devices (SSD) 2024 (pp. 63-9). IEEE.

[19] Prasad J, Chakravarty S, Krishna MV. Lung cancer detection using an integration of fuzzy K-means clustering and deep learning techniques for CT lung images. Bulletin of the Polish Academy of Sciences Technical Sciences. 2022: 1-10.

[20] Sangwan M, Gambhir S, Gupta S. Lung cancer detection using deep learning techniques. In applying AI-based IoT systems to simulation-based information retrieval 2023 (pp. 143-68). IGI Global.

[21] Akram SW, Kolakaluri T, Nagarjuna K, Gayathri M, Teja GBS. Lung cancer detection using deep learning. IJFANS International Journal of Food and Nutritional Sciences. 2022; 11(12).

[22] Asuntha A, Srinivasan A. Deep learning for lung cancer detection and classification. Multimedia Tools and Applications. 2020; 79(11):7731-62.

[23] Elnakib A, Amer HM, Abou-chadi FE. Early lung cancer detection using deep learning optimization. International Journal of Online and Biomedical Engineering. 2020:82-94.

[24] Sajja TK, Devarapalli RM, Kalluri HK. Lung cancer detection based on CT scan images by using deep transfer learning. Traitement Du Signal. 2019; 36(4):339-44.

[25] Kalaivani N, Manimaran N, Sophia S, Devi DD. Deep learning based lung cancer detection and classification. In IOP conference series: materials science and engineering 2020 (pp. 1-6). IOP Publishing.

[26] Pathan S, Ali T, PG S, Rao D. An optimized convolutional neural network architecture for lung cancer detection. APL Bioengineering. 2024; 8(2):1-13.

[27] Teramoto A, Tsukamoto T, Kiriyama Y, Fujita H. Automated classification of lung cancer types from cytological images using deep convolutional neural networks. BioMed Research International. 2017; 2017(1):1-6.

[28] https://www.kaggle.com/datasets/hamdallak/the-iqothnccd-lung-cancer-dataset. Accessed 02 February 2024.

[29] Mosavi A, Ardabili S, Varkonyi-koczy AR. List of deep learning models. In international conference on global research and education 2019 (pp. 202-14). Cham: Springer International Publishing.

[30] Kariuki PW, Gikunda PK, Wandeto JM. Deep transfer learning optimization techniques for medical image classification: a review. In international conference on intelligent computing and machine learning (2ICML) 2023 (pp. 7-15). IEEE.

[31] Bressem KK, Adams LC, Erxleben C, Hamm B, Niehues SM, Vahldiek JL. Comparing different deep learning architectures for classification of chest radiographs. Scientific Reports. 2020; 10(1):1-16.

[32] He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In proceedings of the conference on computer vision and pattern recognition 2016 (pp. 770-8). IEEE.

[33] Abbas ZK, Al-ani AA. An adaptive combination algorithm based on deep learning and genetic algorithm for anomalous events detection. Indonesian Journal of Electrical Engineering and Computer Science. 2023; 31(2):902-8.

[34] Czum JM. Dive into deep learning. Journal of the American College of Radiology. 2020; 17(5):637-8.

[35] Hochreiter S, Schmidhuber J. Long short-term memory. Neural computation. 1997; 9(8):1735-80.

[36] Abbas ZK, Al-ani AA. An adaptive algorithm based on principal component analysis-deep learning for anomalous events detection. Indonesian Journal of Electrical Engineering and Computer Science. 2023; 29(1):421-30.

[37] Sharfuddin AA, Tihami MN, Islam MS. A deep recurrent neural network with bilstm model for sentiment classification. In international conference on Bangla speech and language processing (ICBSLP) 2018 (pp. 1-4). IEEE.

[38] https://docs.radist.online/docs/our-products/radist-web/connections/telegram-bot/instructions-for-creating-and-configuring-a-bot-in-botfather. Accessed 14 February 2024.

[39] https://medium.com/@moraneus/building-telegram-bot-with-python-telegram-bot-a-comprehensive-guide-7e33f014dc79. Accessed 14 February 2024.