Abstract
The area of medical diagnosis has been transformed by computer-aided diagnosis (CAD). With the advancement of technology and the widespread availability of medical data, CAD has gotten a lot of attention, and numerous methods for predicting different pathological diseases have been created. Ultrasound (US) is the safest clinical imaging method; therefore, it is widely utilized in medical and healthcare settings with computer-aided systems. However, owing to patient movement and equipment constraints, certain artefacts make identification of these US pictures challenging. To enhance the quality of pictures for classification and segmentation, certain preprocessing techniques are required. Hence, we proposed a three-stage image segmentation method using U-Net and Iterative Random Forest Classifier (IRFC) to detect orthopedic diseases in ultrasound images efficiently. Initially, the input dataset is preprocessed using Enhanced Wiener Filter for image denoising and image enhancement. Then, the proposed segmentation method is applied. Feature extraction is performed by transform-based analysis. Finally, obtained features are reduced to optimal subset using Principal Component Analysis (PCA). The classification is done using the proposed Iterative Random Forest Classifier. The proposed method is compared with the conventional performance measures like accuracy, specificity, sensitivity, and dice score. The proposed method is proved to be efficient for detecting orthopedic diseases in ultrasound images than the conventional methods.
Highlights
The most frequent bone condition is osteoporosis
There are a variety of imaging diagnostic modalities available, such as Quantitative computed tomography (QCT) [1], UTE [2], DWI [3], DXA [4], and QUS [5]
A computer-aided diagnosis (CAD) method for identifying multiclass kidney problems from ultrasound pictures is proposed in this research
Summary
The most frequent bone condition is osteoporosis. It is a fundamental bone sickness portrayed by diminished natural and inorganic parts of bone tissue per unit volume, which prompts expanded bone construction delicacy and weakness to foundational bone illness described by cracking. For the diagnosis of osteoporosis by BMD (bone mineral density), DXA is the “gold standard.”. At this point, it has become a generally accepted diagnostic tool. Ultrasound (US) systems send and receive sound pulses through the body of the patient These systems are frequently used because of their significant benefits, including the lack of radiation and low cost. The low quality is primarily associated with multireflections of the signals, which results in so-called speckle noise, which lowers brightness, degrades features, and reduces overall image resolution. To overcome this problem, ultrasound (US) is the most straightforward clinical imaging tool.
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