In computer-aided diagnostic technologies, deep convolutional neural image compression classifications are a crucial method. Conventional methods rely primarily on form, colouring, or feature descriptors, and also their configurations, the majority of which would be problem-specific that has been depicted to be supplementary in image data, resulting in a framework that cannot symbolize high problem entities and has poor prototype generalization capability. Emerging Deep Learning (DL) techniques have made it possible to build an end-to-end model, which could potentially general the last detection framework from the raw clinical image dataset. DL methods, on the other hand, suffer from the high computing constraints and costs in analytical modelling and streams owing to the increased mode of accuracy of clinical images and minimal sizes of data. To effectively mitigate these concerns, we provide a techniques and paradigm for DL that blends high-level characteristics generated from a deep network with some classical features in this research. The following stages are involved in constructing the suggested model: Firstly, we supervisedly train a DL model as a coding system, and as a consequence, it could convert raw pixels of medical images into feature extraction, which possibly reflect high-level ideologies for image categorization. Secondly, using image data background information, we derive a collection of conventional characteristics. Lastly, to combine the multiple feature groups produced during the first and second phases, we develop an appropriate method based on deep neural networks. Reference medical imaging datasets are used to assess the suggested method. We get total categorization reliability of 90.1 percent and 90.2 percent, which is greater than existing effective approaches.
Keywords
Deep Learning (DL), Coding Network with Multilayer Perceptron (CNMP), Support Vector Machine (SVM), Convolutional Neural Networks (CNNs)
G. Tian et al., “Adding before pruning: Sparse filter fusion for deep convolutional neural networks via auxiliary attention,” IEEE Trans. Neural Netw. Learn. Syst., vol. PP, 2021.
T. Pladere, M. Velina, V. Andriksone, R. Pitura, K. Panke, and G. Krumina, “Visual search in three-dimensional non-medical images: visual-motor performance of radiologists,” in Fourth International Conference on Applications of Optics and Photonics, 2019.
J. Yuan, A.-T. Chiang, W. Tang, and A. Haro, “EProduct: A million-scale visual search benchmark to address product recognition challenges,” arXiv [cs.CV], 2021.
O. Kunickaya et al., “Using machine vision to improve the efficiency of lumber mills,” J. Phys. Conf. Ser., vol. 1478, no. 1, p. 012020, 2020.
J. Metan, A. Y. Prasad, K. S. Ananda Kumar, M. Mathapati, and K. K. Patil, “Cardiovascular MRI image analysis by using the bio inspired (sand piper optimized) fully deep convolutional network (Bio-FDCN) architecture for an automated detection of cardiac disorders,” Biomed. Signal Process. Control, vol. 70, no. 103002, p. 103002, 2021.
A. Bedoui and M. Et-tolba, “A deep neural network-based interference mitigation for MIMO-FBMC/OQAM systems,” Front. Comms. Net., vol. 2, 2021.
J. Gu, Y. Lu, and G. Xu, “Mismatched lesions on 18F-FDG PET and 18F-fluciclovine PET images in a patient with metastatic prostate small cell carcinoma,” Clin. Nucl. Med., vol. Publish Ahead of Print, 2021.
M. W. Lafarge and V. H. Koelzer, “Rotation invariance and extensive data augmentation: A strategy for the mitosis domain generalization (MIDOG) challenge,” arXiv [cs.CV], 2021.
S. Pang, Z. Yu, and M. A. Orgun, “A novel end-to-end classifier using domain transferred deep convolutional neural networks for biomedical images,” Comput. Methods Programs Biomed., vol. 140, pp. 283–293, 2017.
M. Liu, L. Chen, X. Du, L. Jin, and M. Shang, “Activated gradients for deep neural networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. PP, pp. 1–13, 2021.
G. Son and Y. Kim, “EEG-based emotion classification for verifying the Korean emotional movie clips with Support Vector Machine (SVM),” Complexity, vol. 2021, pp. 1–14, 2021.
O. Reiter et al., “The differences in clinical and dermoscopic features between in situ and invasive nevus-associated melanomas and de novo melanomas,” J. Eur. Acad. Dermatol. Venereol., vol. 35, no. 5, pp. 1111–1118, 2021.
A. B. Beasley et al., “Low-Pass whole-genome sequencing as a method of determining copy number variations in uveal melanoma tissue samples,” J. Mol. Diagn., vol. 22, no. 3, pp. 429–434, 2020.
D. Pandiar, S. Basheer, P. M. Shameena, S. Sudha, and L. J. Dhana, “Amelanotic melanoma masquerading as a granular cell lesion,” Case Rep. Dent., vol. 2013, p. 924573, 2013.
H. Chegraoui et al., “Object detection improves tumour segmentation in MR images of rare brain tumours,” Cancers (Basel), vol. 13, no. 23, p. 6113, 2021.
D. S. Abou et al., “Preclinical single photon emission computed tomography of alpha particle-emitting radium-223,” Cancer Biother. Radiopharm., vol. 35, no. 7, pp. 520–529, 2020.
C. M. Rumack and M. L. Johnson, “Role of computed tomography and ultrasound in neonatal brain imaging,” J. Comput. Tomogr., vol. 7, no. 1, pp. 17–29, 1983.
J.-Y. Li, Z.-H. Zhan, J. Xu, S. Kwong, and J. Zhang, “Surrogate-assisted hybrid-model estimation of distribution algorithm for mixed-variable hyperparameters optimization in convolutional neural networks,” IEEE Trans. Neural Netw. Learn. Syst., vol. PP, pp. 1–15, 2021.
T. Song, L. Xin, C. Gao, T. Zhang, and Y. Huang, “Quaternionic extended local binary pattern with adaptive structural pyramid pooling for color image representation,” Pattern Recognit., vol. 115, no. 107891, p. 107891, 2021.
X. Peng, X. Gao, and X. Li, “On better training the infinite restricted Boltzmann machines,” Mach. Learn., vol. 107, no. 6, pp. 943–968, 2018.
A. G. Nagesha, G. Mahesh, and N. A. Gowrishankar, “Identifying DDoS attacks in 4G networks using artificial neural networks and principal component analysis,” Int. j. netw. virtual organ., vol. 25, no. 1, p. 14, 2021.
G. Rajesh and A. Chaturvedi, “Data reconstruction in heterogeneous environmental wireless sensor networks using robust tensor principal component analysis,” IEEE Trans. Signal Inf. Process. Netw., vol. 7, pp. 539–550, 2021.
P. Chen, C. Agarwal, and A. Nguyen, “The shape and simplicity biases of adversarially robust ImageNet-trained CNNs,” arXiv [cs.CV], 2020.
E. Bellon et al., “PACS/HIS integration in handling and viewing ICU images generated by a phosphorplate scanner,” in Medical Imaging 1996: PACS Design and Evaluation: Engineering and Clinical Issues, 1996.
S. E. Zіrka, Y. I. Moroz, and C. M. Arturi, “Once again about the indivisibility of the scattered inductance of the transformer,” Electr. Eng. Power Eng., no. 1, pp. 8–17, 2021.
A. Rampun, B. Scotney, P. Morrow, H. Wang, and J. Winder, “Breast density classification using local quinary patterns with various neighbourhood topologies,” J. Imaging, vol. 4, no. 1, p. 14, 2018.
F. Avau, M. Chintinne, S. Baudry, and F. Buxant, “Literature review and case report of bilateral intracystic papillary carcinoma associated with an invasive ductal carcinoma in a male breast,” Breast Dis., vol. 41, no. 1, pp. 5–13, 2022.
E. Ahn, A. Kumar, J. Kim, C. Li, D. Feng, and M. Fulham, “X-ray image classification using domain transferred convolutional neural networks and local sparse spatial pyramid,” in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), 2016.
S. A. Rahim and G. Manson, “Kernel principal component analysis for structural health monitoring and damage detection of an engineering structure under operational loading variations,” J. Fail. Anal. Prev., 2021.
A. Ali, Y. Zhu, and M. Zakarya, “Exploiting dynamic spatio-temporal graph convolutional neural networks for citywide traffic flows prediction,” Neural Netw., vol. 145, pp. 233–247, 2022.
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Author(s) thanks to Dr.Dawei Pan for this research completion and support.
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Dawei Pan
Dawei Pan
School of Automation, Tongji University, Shanghai, China, 200070
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Cite this article
Rose Lu and Dawei Pan, “A Critical Analysis of Biomedical Image Classification on Deep Learning”, Journal of Biomedical and Sustainable Healthcare Applications, vol.1, no.2, pp. 113-123, July 2021. doi: 10.53759/0088/JBSHA202101014.
