Lung Image Classification Using Convolutional Neural Network And Prediction of Different Diseases
DOI:
https://doi.org/10.26438/ijcse/v8i4.1013Keywords:
Medical chatbot, Artificial Intelligence,, Model, Image classification,, Convolutional neural networksAbstract
Usually, the people are not aware about a disease and the treatments pertaining to it. Also, the symptoms which leads to that disease is unclear and uncertain and if these symptoms are identified by the person, he must go through various steps for getting an appointment with the doctor like making a call to the healthcare facility. It is also a tedious job for the receptionist to manage all these telephonic calls and fix an appointment according to the availability of the doctor. After the diagnosis of a patient there could be a possibility that doctor couldn’t diagnose the patient properly or there could be some inaccuracies in the diagnosis. In this paper we have come up with an exceptional solution to both of the above mentioned problems, that is we have used a medical chatbot under proper guidance for booking an appointment with the doctor and we have also have built an artificial intelligence based model that uses image classification technique to diagnose the reports of the patient which will state the results to which what the patient is suffering from. It uses CNN (convolutional neural networks) for the processing of the image. And this model can detect various respiratory related diseases.
References
[1] Mrs. Rashmi Dharwadkar, Dr.Mrs. Neeta A. Deshpande "A Medical ChatBot". International Journal of Computer Trends and Technology (IJCTT) V60(1):41-45 June 2018. ISSN:2231-2803. www.ijcttjournal.org. Published by Seventh Sense Research Group.
[2] Z. Lan, G. Zhou, Y. Duan and W. Yan, "AI-Assisted Prediction on Potential Health Risks with Regular Physical Examination Records," 2018 IEEE Third International Conference on Data Science in Cyberspace (DSC), Guangzhou, 2018, pp. 346-352.
[3] S Suren Makaju, P.W.C Prasad, Abeer Alsadoon, A.K.Singh, A.Elchouemi, “Lung Cancer Detection Using CT Scan Images”, International Journa of Computer Sciences and Engineering, Vol.4, Issue.11, pp.111-117, 2015.
[4] H. A. Shiddieqy, F. I. Hariadi and T. Adiono, "Implementation of deep-learning based image classification on single board computer," 2017 International Symposium on Electronics and Smart Devices (ISESD), Yogyakarta, 2017, pp. 133-137.
[5] Rismiyati and S. Azhari, "Convolutional Neural Network implementation for image-based Salak sortation," 2016 2nd International Conference on Science and Technology-Computer (ICST), Yogyakarta, 2016, pp. 77-82.
[6] C. Huang, S. Ni and G. Chen, "A layer-based structured design of CNN on FPGA," 2017 IEEE 12th International Conference on ASIC (ASICON), Guiyang, 2017, pp. 1037-1040.
[7] S. Albawi, T. A. Mohammed and S. Al-Zawi, "Understanding of a convolutional neural network," 2017 International Conference on Engineering and Technology (ICET), Antalya, 2017, pp. 1-6.
[8 ]N. Tajbakhsh et al., “Convolution Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?.” in IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1299-1312, May 2016.
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