Abstract

: Background: Breast cancer (BC), a diverse cancer, continues to be the second leading cause of cancer-related mortality for women globally. The most effective treatment strategies and the accurate discovery of important biomarkers capable of predicting cancer biology remain unclear because of the varied characteristics of BC. Positron Emission Tomography (PET) Molecular Imaging (MI) has enhanced the characterization of BC; however, there are still limitations to these techniques. Recently, the utilization of DL (Deep Learning) methods has revolutionized the field of medical imaging, enabling the detection and analysis of features often imperceptible to the human eye. Methods Used: With this motivation, this work proposes a novel approach named Dynamic Grade Weighted Switching Median Filter (DGWSMF) to remove impulsive noise issues in colour digital images. This study introduces a novel approach to the Spatial Adaptive Fuzzy C-Means (SAFCM) clustering procedure on PET scan image datasets for segmenting affected regions. Then, propose an Optimal Deep Convolutional Spiking Neural Network (ODCSNN) with direct training to classify the BC in PET images. The network weights are then optimally selected by the Adaptive Nut-Finding Algorithm (ANFA) to provide higher accuracy and a proposed framework named NeuroPET. Results Achieved: The results show that the NeuroPET using gradient surrogate method can achieve a high accuracy of 95.0235%, comparable to traditional methods. Concluding Remarks: Here, we also present a comparison with existing learning and a converted algorithm, and the proposed NeuroPET is proven to have the best performance.

Keyword

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Refference

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[13]Riad AM, Elminir HK, Shohieb SM. Hand gesture recognition system based on a geometric model and rule based classifier. British Journal of Applied Science & Technology. 2014; 4(9):1432-44.

[14]Mariappan HM, Gomathi V. Real-time recognition of Indian sign language. In international conference on computational intelligence in data science (ICCIDS) 2019 (pp. 1-6). IEEE.

[15]Wu J, Sun L, Jafari R. A wearable system for recognizing American sign language in real-time using IMU and surface EMG sensors. IEEE Journal of Biomedical and Health Informatics. 2016; 20(5):1281-90.

[16]Rekha J, Bhattacharya J, Majumder S. Hand gesture recognition for sign language: a new hybrid approach. In proceedings of the international conference on image processing, computer vision, and pattern recognition (IPCV) 2011 (pp. 1-7). WorldComp.

[17]Huu PN, Phung NT. Hand gesture recognition algorithm using SVM and HOG model for control of robotic system. Journal of Robotics. 2021; 2021:1-3.

[18]Shinde P, Shinde P, Shinde S, Shinde S, Shinde S. Augmented reptile feeder. In Pune section international conference (PuneCon) 2022 (pp. 1-4). IEEE.

[19]Ismail MH, Dawwd SA, Ali FH. A review on Arabic sign language recognition. Journal of Advances in Computer and Electronics Engineering. 2021; 6(12):1-12.

[20]Michele A, Colin V, Santika DD. Mobilenet convolutional neural networks and support vector machines for palmprint recognition. Procedia Computer Science. 2019; 157:110-7.

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