1
Department of Computer Science and Engineering Nitte Meenakshi Institute of Technology Bengaluru India
(vani.v@nmit.ac.in)
2
Department of Artificial Intelligence and Machine Learning Nitte Meenakshi Institute of Technology Bengaluru India
(piyush.kumar@nmit.ac.in)
Abstract :
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.
Keywords :
CAD; CNN; MIL; DCNN.
References :
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| Style | # |
|---|---|
| MLA | Vani V. , Piyush Kumar Pareek. "Deep Multiple Instance Learning Approach for Classification in Clinical Decision Support Systems." American Journal of Business and Operations Research, Vol. 10, No. 2, 2023 ,PP. 52-60 (Doi : https://doi.org/10.54216/AJBOR.100206) |
| APA | Vani V. , Piyush Kumar Pareek. (2023). Deep Multiple Instance Learning Approach for Classification in Clinical Decision Support Systems. Journal of American Journal of Business and Operations Research, 10 ( 2 ), 52-60 (Doi : https://doi.org/10.54216/AJBOR.100206) |
| Chicago | Vani V. , Piyush Kumar Pareek. "Deep Multiple Instance Learning Approach for Classification in Clinical Decision Support Systems." Journal of American Journal of Business and Operations Research, 10 no. 2 (2023): 52-60 (Doi : https://doi.org/10.54216/AJBOR.100206) |
| Harvard | Vani V. , Piyush Kumar Pareek. (2023). Deep Multiple Instance Learning Approach for Classification in Clinical Decision Support Systems. Journal of American Journal of Business and Operations Research, 10 ( 2 ), 52-60 (Doi : https://doi.org/10.54216/AJBOR.100206) |
| Vancouver | Vani V. , Piyush Kumar Pareek. Deep Multiple Instance Learning Approach for Classification in Clinical Decision Support Systems. Journal of American Journal of Business and Operations Research, (2023); 10 ( 2 ): 52-60 (Doi : https://doi.org/10.54216/AJBOR.100206) |
| IEEE | Vani V., Piyush Kumar Pareek, Deep Multiple Instance Learning Approach for Classification in Clinical Decision Support Systems, Journal of American Journal of Business and Operations Research, Vol. 10 , No. 2 , (2023) : 52-60 (Doi : https://doi.org/10.54216/AJBOR.100206) |