Abstract

Breast cancer (BC), a heterogeneous disease, remains the second leading cause of cancer-related mortality among women globally. The development of effective treatment strategies and the identification of critical biomarkers capable of predicting cancer progression remains challenging due to the diverse characteristics of BC. While positron emission tomography (PET) and molecular imaging (MI) have enhanced the characterization of BC, these techniques still face certain limitations. Recently, deep learning (DL) methods have revolutionized medical imaging by enabling the detection and analysis of features often imperceptible to the human eye. To address these limitations, NeuroPET, a novel framework, was proposed to enhance the accuracy and reliability of PET imaging for BC detection and classification. NeuroPET integrates several advanced methods, including the dynamic grade weighted switching median filter (DGWSMF), designed to mitigate impulsive noise in colour digital images, and a novel spatial adaptive fuzzy c-means (SAFCM) clustering method, developed to segment affected regions in PET scan datasets. At the core of NeuroPET is the optimal deep convolutional spiking neural network (ODCSNN), which employs direct training for PET image classification. The network's weights are optimized using the adaptive nut-finding algorithm (ANFA), enabling superior performance. NeuroPET further utilizes the gradient surrogate method to achieve a high classification accuracy of 95.02%, surpassing traditional approaches. Comparative analysis demonstrates that NeuroPET significantly outperforms existing techniques, underscoring its potential as an advanced BC detection, classification, and analysis tool.

Keywords

Machine learning (ML), Breast cancer (BC), Positron emission tomography (PET), Data mining (DM).

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