Detection of Brain Tumor from MRI Images Using Deep Dense Neural Network
Keywords:
ADF, Classification, CLAHE, Deep learning, Deep dense neural network, Grayscale, PCAAbstract
The classification of Brain tumors is fundamental for the finding of Brain Cancer (BC) in medical services frameworks. AI (artificial intelligence) methods in light of PC-supported analytic frameworks (Miscreants) are generally utilized for the exact location of brain tumors. In any case, because of the issues like the mistake of counterfeit demonstrative frameworks, clinical experts are not really integrating them into the conclusion cycle of Brain tumors (BT). As deep learning (DL) technology got revolutionized greatly in medical fields, the usage of such ideas brings more effectiveness in terms of performance and accuracy. With that said, this research work offers an efficient deep learning-based categorization of brain tumors, with the steps being as follows: a) Data collecting from well-known databases for the brain, lung, and liver, which together comprise 10,000 records, b) Preprocessing using CLAHE ( for brightness enhancing), Thresholding (Grayscale), Filtering (ADF) and skull masking for removal of noise and anomalies from raw images, c) feature extraction using Principle Component Analysis (PCA), d) feature selection using VGG16 network and finally e) classification using Deep Dense Neural Network (Densenet 164). Experimental tests indicates that the proposed model outperforms better than other state-of-art models under different measures (accuracy: 0.97, sensitivity: 0.98, specificity: 0.98, detection rate: 0.94).
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M. Akil, R. Saouli, and R. Kachouri, “Fully automatic brain tumor segmentation with deep learning-based selective attention using overlapping patches and multi-class weighted cross-entropy,” Medical Image Analysis, vol. 63, Article ID 101692, 2020.
H. Habib, R. Amin, B. Ahmed, and A. Hannan, “Hybrid algorithms for brain tumor segmentation, classification and feature extraction,” Journal of Ambient Intelligence and Humanized Computing, vol. 119, pp. 1–22, 2021.
M. N. Ullah, Y. Park, G. B. Kim et al., “Simultaneous acquisition of ultrasound and gamma signals with a single-channel readout,” Sensors, vol. 21, no. 4, p. 1048, 2021.
F. Jamil, M. A. Iqbal, R. Amin, and D. Kim, “Adaptive thermal-aware routing protocol for wireless body area network,” Electronics, vol. 8, no. 1, p. 47, 2019
S. Deepak and P. Ameer, “Brain tumor classification using deep CNN features via transfer learning,” Computers in Biology and Medicine, vol. 111, Article ID 103345, 2020.
Mohsen, H., El-Dahshan, E. S. A., El-Horbaty, E. S. M., & Salem, A. B. M. (2018). Classification using deep learning neural networks for brain tumors. Future Computing and Informatics Journal, 3(1), 68-71.
Almadhoun, H. R., & Abu-Naser, S. S. (2022). Detection of Brain Tumor Using Deep Learning. International Journal of Academic Engineering Research (IJAER), 6(3).
Siar, M., & Teshnehlab, M. (2019, October). Brain tumor detection using deep neural network and machine learning algorithm. In 2019 9th international conference on computer and knowledge engineering (ICCKE) (pp. 363-368). IEEE.
Naser, M. A., & Deen, M. J. (2020). Brain tumor segmentation and grading of lowergrade glioma using deep learning in MRI images. Computers in biology and medicine, 121, 103758.
Sun, L., Zhang, S., Chen, H., & Luo, L. (2019). Brain tumor segmentation and survival prediction using multimodal MRI scan with deep learning. Frontiers in neuroscience, 13, 810.
Alqudah, A. M., Alquraan, H., Qasmieh, I. A., Alqudah, A., & Al-Sharu, W. (2020). Brain tumor classification using deep learning technique--a comparison between cropped, uncropped, and segmented lesion images with different sizes. arXiv preprint arXiv:2001.08844.
Sharif, M. I., Khan, M. A., Alhussein, M., Aurangzeb, K., & Raza, M. (2022). A decision support system for multimodal brain tumor classification using deep learning. Complex & Intelligent Systems, 8(4), 3007-3020.
Kang, J., Ullah, Z., & Gwak, J. (2021). Mri-based brain tumor classification using an ensemble of deep features and machine learning classifiers. Sensors, 21(6), 2222.
Gunasekara, S. R., Kaldera, H. N. T. K., & Dissanayake, M. B. (2021). A systematic approach for MRI brain tumor localization and segmentation using deep learning and active contouring. Journal of Healthcare Engineering, 2021.
Gurunathan, A., & Krishnan, B. (2021). Detection and diagnosis of brain tumors using deep learning convolutional neural networks. International Journal of Imaging Systems and Technology, 31(3), 1174-1184. [16] Polat, Ö., & Güngen, C. (2021). Classification of brain tumors from MR images using deep transfer learning. The Journal of Supercomputing, 77(7), 7236-7252.
Thillaikkarasi, R., & Saravanan, S. (2019). An enhancement of deep learning algorithm for brain tumor segmentation using kernel-based CNN with M-SVM. Journal of medical systems, 43(4), 1-7.
Abd Khalid, N. E., Ismail, M. F., Manaf, M. A. A., Fadzil, A. F. A., & Ibrahim, S. (2020). MRI brain tumor segmentation: A forthright image processing approach. Bulletin of Electrical Engineering and Informatics, 9(3), 1024-1031.
Maurya, R., & Wadhwani, S. (2022). An efficient method for brain image preprocessing with anisotropic diffusion filter & tumor segmentation. Optik, 265, 169474.
Rajesh, M. (2021). Preprocessing and Skull Stripping of Brain Tumor Extraction from Magnetic Resonance Imaging Images Using Image Processing. Recent Trends in Intensive Computing, 39, 299.
Gaikwad, S. B., & Joshi, M. S. (2015). Brain tumor classification using principal component analysis and probabilistic neural network. International Journal of Computer Applications, 120(3).
Hamid, M. A., & Khan, N. A. (2020). Investigation and classification of MRI brain tumors using feature extraction technique. Journal of Medical and Biological Engineering, 40(2), 307-317.
Gumaei, A., Hassan, M. M., Hassan, M. R., Alelaiwi, A., & Fortino, G. (2019). A hybrid feature extraction method with regularized extreme learning machine for brain tumor classification. IEEE Access, 7, 36266-36273.
Zhou, Y., Li, Z., Zhu, H., Chen, C., Gao, M., Xu, K., & Xu, J. (2018, September). Holistic brain tumor screening and classification based on densenet and recurrent neural network. In International MICCAI Brainlesion Workshop (pp. 208-217). Springer, Cham.
Nawaz, M., Nazir, T., Masood, M., Mehmood, A., Mahum, R., Khan, M. A., & Thinnukool, O. (2021). Analysis of brain MRI images using improved cornernet approach. Diagnostics, 11(10), 1856.
Hu, J., Gu, X., & Gu, X. (2021). Dual‐pathway DenseNets with fully lateral connections for multimodal brain tumor segmentation. International Journal of Imaging Systems and Technology, 31(1), 364-378.
Masood, M., Nazir, T., Nawaz, M., Mehmood, A., Rashid, J., Kwon, H. Y., & Hussain, A. (2021). A novel deep learning method for recognition and classification of brain tumors from MRI images. Diagnostics, 11(5), 744.
Kalpana, R., Bennet, M. A., & Rahmani, A. W. (2022). Metaheuristic OptimizationDriven Novel Deep Learning Approach for Brain Tumor Segmentation. BioMed Research International, 2022.
Sudeepthi Govathoti, A Mallikarjuna Reddy, Deepthi Kamidi, G BalaKrishna, Sri Silpa Padmanabhuni and Pradeepini Gera, “Data Augmentation Techniques on Chilly Plants to Classify Healthy and Bacterial Blight Disease Leaves” International Journal of Advanced Computer Science and Applications(IJACSA), 13(6), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130618
V. NavyaSree, Y. Surarchitha, A. M. Reddy, B. Devi Sree, A. Anuhya and H. Jabeen, "Predicting the Risk Factor of Kidney Disease using Meta Classifiers," 2022 IEEE 2nd Mysore Sub Section International Conference (MysuruCon), 2022, pp. 1-6, doi: 10.1109/MysuruCon55714.2022.9972392.
B. H. Rao et al., "MTESSERACT: An Application for Form Recognition in Courier Services," 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), 2022, pp. 848-853, doi: 10.1109/ICOSEC54921.2022.9952031
P. S. Silpa et al., "Designing of Augmented Breast Cancer Data using Enhanced Firefly Algorithm," 2022 3rd International Conference on Smart Electronics and Communication (ICOSEC), 2022, pp. 759-767, doi: 10.1109/ICOSEC54921.2022.9951883.
P. Grandhe, A. M. Reddy, K. Chillapalli, K. Koppera, M. Thambabathula and L. P. Reddy Surasani, "Improving The Hiding Capacity of Image Steganography with Stego-Analysis," 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), Raichur, India, 2023, pp. 01-06, doi: 10.1109/ICICACS57338.2023.10100146.
Jenifa Sabeena, S. ., & Antelin Vijila, S. . (2023). Moulded RSA and DES (MRDES) Algorithm for Data Security. International Journal on Recent and Innovation Trends in Computing and Communication, 11(2), 154–162. https://doi.org/10.17762/ijritcc.v11i2.6140
Prof. Prachiti Deshpande. (2016). Performance Analysis of RPL Routing Protocol for WBANs. International Journal of New Practices in Management and Engineering, 5(01), 14 - 21. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/43
Aoudni, Y., Donald, C., Farouk, A., Sahay, K. B., Babu, D. V., Tripathi, V., & Dhabliya, D. (2022). Cloud security based attack detection using transductive learning integrated with hidden markov model. Pattern Recognition Letters, 157, 16-26. doi:10.1016/j.patrec.2022.02.012
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