{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T22:05:49Z","timestamp":1757541949854},"reference-count":19,"publisher":"IGI Global","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,4,1]]},"abstract":"<p>Implementing image processing tools demands its components produce better results in critical applications like medical image classification. TensorFlow is one open source with a machine learning framework for high performance and operates in heterogeneous environments. It heralds broad attention at a fine tuning of parameters for obtaining the final models, to obtain better performance. The main aim of this article is to prove the appropriate steps for the classification techniques for diagnosing the diseases with better accuracy. The proposed convolutional network is comprised of three convolutional layers, preceded by average pooling with a size equal to the size of the final feature maps. The final layer in this network has two outputs, corresponding to the number of classes considered to be either normal or abnormal. To train and evaluate such networks like the Deep Convolutional Neural Network (DCNN), a dataset of 2000 x-ray images of lungs was used and a comparative analysis between the proposed DCNN against previous methods is also made.<\/p>","DOI":"10.4018\/ijcvip.2019040101","type":"journal-article","created":{"date-parts":[[2019,3,27]],"date-time":"2019-03-27T18:33:04Z","timestamp":1553711584000},"page":"1-15","source":"Crossref","is-referenced-by-count":2,"title":["Construction of Deep Convolutional Neural Networks For Medical Image Classification"],"prefix":"10.4018","volume":"9","author":[{"family":"Rama A","sequence":"first","affiliation":[{"name":"Bharath Institute of Higher Education and Research, Chennai, India"}]},{"family":"Kumaravel A","sequence":"additional","affiliation":[{"name":"Bharath Institute of Science and Technology, Chennai, India"}]},{"family":"Nalini C","sequence":"additional","affiliation":[{"name":"Bharath Institute of Science and Technology, Chennai, India"}]}],"member":"2432","reference":[{"key":"IJCVIP.2019040101-0","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., . . . Kudlur, M. (2016, November). Tensorflow: a system for large-scale machine learning. In OSDI (Vol. 16, pp. 265-283)."},{"issue":"5","key":"IJCVIP.2019040101-1","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1109\/TMI.2016.2535865","article-title":"Lung pattern classification for interstitial lung diseases using a deep convolutional neural network.","volume":"35","author":"M.Anthimopoulos","year":"2016","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"IJCVIP.2019040101-2","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1007\/978-3-319-42999-1_2","article-title":"Review of deep learning methods in mammography, cardiovascular, and microscopy image analysis","author":"G.Carneiro","year":"2017","journal-title":"Deep Learning and Convolutional Neural Networks for Medical Image Computing"},{"key":"IJCVIP.2019040101-3","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098171"},{"key":"IJCVIP.2019040101-4","doi-asserted-by":"publisher","DOI":"10.1109\/ICPR.2016.7900116"},{"key":"IJCVIP.2019040101-5","doi-asserted-by":"crossref","first-page":"46479","DOI":"10.1038\/srep46479","article-title":"Towards automatic pulmonary nodule management in lung cancer screening with deep learning.","volume":"7","author":"F.Ciompi","year":"2017","journal-title":"Scientific Reports"},{"key":"IJCVIP.2019040101-6","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocv080"},{"key":"IJCVIP.2019040101-7","unstructured":"Islam, M. T., Aowal, M. A., Minhaz, A. T., & Ashraf, K. (2017). Abnormality detection and localization in chest x-rays using deep convolutional neural networks. arXiv:1705.09850 Retrieved from https:\/\/arxiv.org\/pdf\/1705.09850.pdf"},{"issue":"6","key":"IJCVIP.2019040101-8","first-page":"475","article-title":"Two public chest x-ray datasets for computer-aided screening of pulmonary diseases.","volume":"4","author":"S.Jaeger","year":"2014","journal-title":"Quantitative Imaging in Medicine and Surgery"},{"key":"IJCVIP.2019040101-9","doi-asserted-by":"crossref","unstructured":"Khan, S., & Yong, S.-P. (2017). A Deep Learning Architecture for Classifying Medical Images of Anatomy Object. In Proceedings of APSIPA Annual Summit and Conference, Malaysia. Retrieved from https:\/\/ieeexplore.ieee.org\/document\/8282299","DOI":"10.1109\/APSIPA.2017.8282299"},{"key":"IJCVIP.2019040101-10","first-page":"749","article-title":"ImageNet Classification with Deep Convolutional Neural Networks.","volume":"107","author":"A.Krizhevsky","year":"2017","journal-title":"Procedia Computer Science"},{"key":"IJCVIP.2019040101-11","doi-asserted-by":"publisher","DOI":"10.1109\/ISBB.2014.6820918"},{"key":"IJCVIP.2019040101-12","doi-asserted-by":"crossref","unstructured":"Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D. D., & Chen, M. (2014, December). Medical image classification with convolutional neural network. In 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV) (pp. 844-848). IEEE.","DOI":"10.1109\/ICARCV.2014.7064414"},{"key":"IJCVIP.2019040101-13","doi-asserted-by":"crossref","unstructured":"Manessi, F., & Rozza, A. (2018). Learning Combinations of Activation Functions. arXiv:1801.09403","DOI":"10.1109\/ICPR.2018.8545362"},{"key":"IJCVIP.2019040101-14","doi-asserted-by":"publisher","DOI":"10.1016\/j.fcij.2017.12.001"},{"key":"IJCVIP.2019040101-15","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/978-3-319-65981-7_12","article-title":"Deep Learning for Medical Image Processing: Overview, Challenges and the Future","author":"M. I.Razzak","year":"2018","journal-title":"Classification in BioApps"},{"key":"IJCVIP.2019040101-16","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.procs.2016.09.365","article-title":"Biometric retina identification based on neural network.","volume":"102","author":"F.Sadikoglu","year":"2016","journal-title":"Procedia Computer Science"},{"key":"IJCVIP.2019040101-17","doi-asserted-by":"publisher","DOI":"10.2214\/ajr.174.1.1740071"},{"key":"IJCVIP.2019040101-18","unstructured":"Zhang, J., Xia, Y., Wu, Q., & Xie, Y. (2017). Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning. arXiv:1706.09092"}],"container-title":["International Journal of Computer Vision and Image Processing"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=226241","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,6]],"date-time":"2022-05-06T21:02:04Z","timestamp":1651870924000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJCVIP.2019040101"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2019,4,1]]},"references-count":19,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2019,4]]}},"URL":"https:\/\/doi.org\/10.4018\/ijcvip.2019040101","relation":{},"ISSN":["2155-6997","2155-6989"],"issn-type":[{"value":"2155-6997","type":"print"},{"value":"2155-6989","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,1]]}}}