A hybrid thyroid tumor type classification system using feature fusion, multilayer perceptron and bonobo optimization.

IF 1.7 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION
B Shankarlal, S Dhivya, K Rajesh, S Ashok
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Abstract

Background: Thyroid tumor is considered to be a very rare form of cancer. But recent researches and surveys highlight the fact that it is becoming prevalent these days because of various factors.

Objectives: This paper proposes a novel hybrid classification system that is able to identify and classify the above said four different types of thyroid tumors using high end artificial intelligence techniques. The input data set is obtained from Digital Database of Thyroid Ultrasound Images through Kaggle repository and augmented for achieving a better classification performance using data warping mechanisms like flipping, rotation, cropping, scaling, and shifting.

Methods: The input data after augmentation goes through preprocessing with the help of bilateral filter and is contrast enhanced using dynamic histogram equalization. The ultrasound images are then segmented using SegNet algorithm of convolutional neural network. The features needed for thyroid tumor classification are obtained from two different algorithms called CapsuleNet and EfficientNetB2 and both the features are fused together. This process of feature fusion is carried out to heighten the accuracy of classification.

Results: A Multilayer Perceptron Classifier is used for classification and Bonobo optimizer is employed for optimizing the results produced. The classification performance of the proposed model is weighted using metrics like accuracy, sensitivity, specificity, F1-score, and Matthew's correlation coefficient.

Conclusion: It can be observed from the results that the proposed multilayer perceptron based thyroid tumor type classification system works in an efficient manner than the existing classifiers like CANFES, Spatial Fuzzy C means, Deep Belief Networks, Thynet and Generative adversarial network and Long Short-Term memory.

使用特征融合、多层感知器和Bonobo优化的混合甲状腺肿瘤类型分类系统。
背景:甲状腺肿瘤是一种非常罕见的癌症:甲状腺肿瘤被认为是一种非常罕见的癌症。但最近的研究和调查突出表明,由于各种因素的影响,甲状腺肿瘤正变得越来越普遍:本文提出了一种新型混合分类系统,该系统能够利用高端人工智能技术对上述四种不同类型的甲状腺肿瘤进行识别和分类。输入数据集来自 Kaggle 存储库中的甲状腺超声图像数字数据库,并通过翻转、旋转、裁剪、缩放和移位等数据扭曲机制进行增强,以获得更好的分类性能:扩增后的输入数据在双边滤波器的帮助下进行预处理,并利用动态直方图均衡化增强对比度。然后使用卷积神经网络的 SegNet 算法对超声图像进行分割。甲状腺肿瘤分类所需的特征可从 CapsuleNet 和 EfficientNetB2 两种不同的算法中获取,并将两种特征融合在一起。进行特征融合的目的是为了提高分类的准确性:使用多层感知器分类器进行分类,并使用 Bonobo 优化器对分类结果进行优化。使用准确率、灵敏度、特异性、F1-分数和马修相关系数等指标对拟议模型的分类性能进行加权:从结果可以看出,与现有的分类器(如 CANFES、空间模糊 C means、深度信念网络、Thynet 和生成式对抗网络以及长短期记忆)相比,基于多层感知器的甲状腺肿瘤类型分类系统的工作效率更高。
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来源期刊
CiteScore
4.90
自引率
23.30%
发文量
150
审稿时长
3 months
期刊介绍: Research areas within the scope of the journal include: Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes
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