2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 10 , Issue 2 , PP: 52-60, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Vani V. 1 * , Piyush Kumar Pareek 2
Doi: https://doi.org/10.54216/AJBOR.100206
To get around the drawbacks of conventional classification algorithms that required manual feature extraction and the high computational cost of neural networks, this paper introduces a deep convolutional neural network with multiple instance learning approaches, namely dynamic max pooling and sparse representation. For the categorization of tuberculosis lung illness, this model combines deep convolutional neural networks and multiple instance learning. The design was composed of four phases: pre-processing, instance production, feature extraction, and classification. To perform feature extraction, a model based on a customized version of the VGG16 architecture was trained from scratch. Multiple instance learning techniques such as Diverse Density (DD) and the Maximum pattern bag formulation of the Support Vector Machine were used to evaluate how well the proposed classification algorithm performed in comparison (SVM).The numerical findings demonstrated that the new method offered a higher level of accuracy than the methods that had been used in the past. When evaluating the efficacy of the current method, accuracy, specificity, sensitivity, and error rate were all taken into consideration. The accuracy of the max-pooling based framework and the sparse representation framework was found to be greater than that of the other multiple instance strategies, coming in at 91.51% and 89.84%, respectively, when compared to that of the other methods. The improved accuracy of the present system that makes use of deep neural networks is mostly attributable to the contributions made by features such as transfer learning and automatic feature extraction.
CAD , CNN , MIL , DCNN.
[1] R.T. Sutton, D. Pincock, D.C. Baumgart, D.C. Sadowski, R.N. Fedorak, K.I. KroekerAn overview of clinical decision support systems: benefits, risks, and strategies for successNPJ Digit. Med., 3 (2020), p. 17
[2] L. Moja, K.H. Kwag, T. Lytras, L. Bertizzolo, L. Brandt, V. Pecoraro, G. Rigon, A. Vaona, F. Ruggiero, M. Mangia, et al. Effectiveness of computerized decision support systems linked to electronic health records: a systematic review and meta-analysis Am. J. Public Health, 104 (2014), pp. e12-e22
[3] J. Varghese, M. Kleine, S.I. Gessner, S. Sandmann, M. Dugas Effects of computerized decision support system implementations on patient outcomes in inpatient care: a systematic reviewJ. Am. Med. Inform. Assoc., 25 (2018), pp. 593-602
[4] S. Benjamens, P. Dhunnoo, B. Meskó The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database NPJ Digit. Med., 3 (2020), p. 118
[5] J. Dong, Y. Geng, D. Lu, B. Li, L. Tian, D. Lin, Y. Zhang Clinical trials for artificial intelligence in cancer diagnosis: a cross-sectional study of registered trials in Clinical Trials. Gov Front. Oncol., 10 (2020), p. 1629
[6] A. Rajkomar, J. Dean, I. Kohane Machine learning in medicine N. Engl. J. Med., 380 (2019), pp. 1347-1358
[7] E.J. Topol High-performance medicine: the convergence of human and artificial intelligence Nat. Med., 25 (2019), pp. 44-56
[8] Masud, M.; Hossain, M.S.; Alhumyani, H.; Alshamrani, S.S.; Cheikhrouhou, O.; Ibrahim, S.; Muhammad, G.; Rashed, A.E.E.; Gupta, B.B. Pre-Trained Convolutional Neural Networks for Breast Cancer Detection Using Ultrasound Images. ACM Trans. Internet Technol. 2021, 21, 85.
[9] Thigpen, D.; Kappler, A.; Brem, R. The Role of Ultrasound in Screening Dense Breasts—A Review of the Literature and Practical Solutions for Implementation. Diagnostics 2018, 8, 20.
[10] Muhammad, M.; Zeebaree, D.; Brifcani, A.M.A.; Saeed, J.; Zebari, D.A. Region of interest segmentation based on clustering techniques for breast cancer ultrasound images: A review. J. Appl. Sci. Technol. Trends 2020, 1, 78–91.
[11] Wang, N.; Bian, C.; Wang, Y.; Xu, M.; Qin, C.; Yang, X.; Wang, T.; Li, A.; Shen, D.; Ni, D. Densely Deep Supervised Networks with Threshold Loss for Cancer Detection in Automated Breast Ultrasound. In Proceedings of the International Conference on Medical Image Computing and Computer—Assisted Intervention, Granada, Spain, 16–20 September 2018; pp. 641–648.
[12]Guo, R.; Lu, G.; Qin, B.; Fei, B. Ultrasound Imaging Technologies for Breast Cancer Detection and Management: A Review. Ultrasound Med. Biol. 2018, 44, 37–70.
[13] Zhang, X.; Lin, X.; Tan, Y.; Zhu, Y.; Wang, H.; Feng, R.; Tang, G.; Zhou, X.; Li, A.; Qiao, Y. A multicenter hospital-based diagnosis study of automated breast ultrasound system in detecting breast cancer among Chinese women. Chin. J. Cancer Res. 2018, 30, 231.
[14] Mohammed, M.A.; Al-Khateeb, B.; Rashid, A.N.; Ibrahim, D.A.; Abd Ghani, M.K.; Mostafa, S.A. Neural net-work and multi-fractal dimension features for breast cancer classification from ultrasound images. Comput. Electr. Eng. 2018, 70, 871–882.
[15] Wang, Y.; Wang, N.; Xu, M.; Yu, J.; Qin, C.; Luo, X.; Yang, X.; Wang, T.; Li, A.; Ni, D. Deeply-supervised net-works with threshold loss for cancer detection in automated breast ultrasound. IEEE Trans. Med. Imaging 2019, 39, 866–876.
[16] Shen, L.; Margolies, L.R.; Rothstein, J.H.; Fluder, E.; McBride, R.; Sieh, W. Deep Learning to Improve Breast Cancer Detection on Screening Mammography. Sci. Rep. 2019, 9, 12495.
[17] K. Kiruthika , S. Gayathri, R. Hemalatha, P. Menaga, Design and Development of Mobile Healthcare Application for “Ayurvedic” based Clinical Documents, Journal of Cognitive Human-Computer Interaction, Vol. 1 , No. 1 , (2021) : 18-27 (Doi : https://doi.org/10.54216/JCHCI.010103)
[18] Badawy, S.M.; Mohamed, A.E.N.A.; Hefnawy, A.A.; Zidan, H.E.; GadAllah, M.T.; El-Banby, G.M. Automatic semantic segmentation of breast tumors in ultrasound images based on combining fuzzy logic and deep learning—A feasibility study. PLoS ONE 2021, 16, e0251899.
[19] Almajalid, R.; Shan, J.; Du, Y.; Zhang, M. Development of a deep-learning-based method for breast ultrasound image segmentation. In Proceedings of the 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA, 17–20 December 2018; pp. 1103–1108.
[20] Kalafi, E.Y.; Jodeiri, A.; Setarehdan, S.K.; Lin, N.W.; Rahmat, K.; Taib, N.A.; Ganggayah, M.D.; Dhillon, S.K. Classification of Breast Cancer Lesions in Ultrasound Images by Using Attention Layer and Loss Ensemble in Deep Convolutional Neural Networks. Diagnostics 2021, 11, 1859.
[21] Cao, Z.; Duan, L.; Yang, G.; Yue, T.; Chen, Q. An experimental study on breast lesion detection and classification from ultrasound images using deep learning architectures. BMC Med. Imaging 2019, 19, 51
[22] Roy V., Shukla S. (2013) Image Denoising by Data Adaptive and Non-Data Adaptive Transform Domain Denoising Method Using EEG Signal. In: Kumar V., Bhatele M. (eds) Proceedings of All India Seminar on Biomedical Engineering 2012 (AISOBE 2012). Lecture Notes in Bioengineering. Springer, India. https://doi.org/10.1007/978-81-322-0970-6_2.
[23]Qi, X.; Yi, F.; Zhang, L.; Chen, Y.; Pi, Y.; Chen, Y.; Guo, J.; Wang, J.; Guo, Q.; Li, J.; et al. Computer-aided Diagnosis of Breast Cancer in Ultrasonography Images by Deep learning. Neuro-computing 2021, 472, 152–165.
[24] Shankar, K.; Perumal, E.; Tiwari, P.; Shorfuzzaman, M.; Gupta, D. Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images. Multimedia Syst. 2021, 2021, 1–13.
