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. 2022 Aug 10:10:1800712.
doi: 10.1109/JTEHM.2022.3197923. eCollection 2022.

Particle Swarm Optimized Fuzzy CNN With Quantitative Feature Fusion for Ultrasound Image Quality Identification

Muhammad Minoar Hossain  1 , Md Mahmodul Hasan  1 , Md Abdur Rahim  1 , Mohammad Motiur Rahman  1 , Mohammad Abu Yousuf  2 , Samer Al-Ashhab  3 , Hanan F Akhdar  4 , Salem A Alyami  3 , Akm Azad  5 , Mohammad Ali Moni  6
Affiliations

Particle Swarm Optimized Fuzzy CNN With Quantitative Feature Fusion for Ultrasound Image Quality Identification

Muhammad Minoar Hossain et al. IEEE J Transl Eng Health Med. .

Abstract

Inherently ultrasound images are susceptible to noise which leads to several image quality issues. Hence, rating of an image's quality is crucial since diagnosing diseases requires accurate and high-quality ultrasound images. This research presents an intelligent architecture to rate the quality of ultrasound images. The formulated image quality recognition approach fuses feature from a Fuzzy convolutional neural network (fuzzy CNN) and a handcrafted feature extraction method. We implement the fuzzy layer in between the last max pooling and the fully connected layer of the multiple state-of-the-art CNN models to handle the uncertainty of information. Moreover, the fuzzy CNN uses Particle swarm optimization (PSO) as an optimizer. In addition, a novel Quantitative feature extraction machine (QFEM) extracts hand-crafted features from ultrasound images. Next, the proposed method uses different classifiers to predict the image quality. The classifiers categories ultrasound images into four types (normal, noisy, blurry, and distorted) instead of binary classification into good or poor-quality images. The results of the proposed method exhibit a significant performance in accuracy (99.62%), precision (99.62%), recall (99.61%), and f1-score (99.61%). This method will assist a physician in automatically rating informative ultrasound images with steadfast operation in real-time medical diagnosis.

Keywords: Ultrasound image; feature extraction; feature fusion; fuzzy convolutional neural network; particle swarm optimization (PSO); quantitative feature extraction machine (QFEM).

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Figures

FIGURE 1.
FIGURE 1.
The fundamental architecture of this research.
FIGURE 2.
FIGURE 2.
Structure of QFEM.
FIGURE 3.
FIGURE 3.
Mechanism of QFEM for a noisy image of the dataset.
FIGURE 4.
FIGURE 4.
Cluster-wise visualization of the features of QFEM.
FIGURE 5.
FIGURE 5.
Illustration of proposed fuzzy layer-based VGG-19 fine-tuning by utilizing PSO.
FIGURE 6.
FIGURE 6.
Demonstration of different parameters used to find performance measurement metrics.
FIGURE 7.
FIGURE 7.
NCM of the proposed scheme.
FIGURE 8.
FIGURE 8.
Roc curve of the proposed scheme, where classes 0,1,2 and 3 present Normal, Noisy, Blurry, and Distorted ultrasound images respectively.
See this image and copyright information in PMC

References

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