Computer-Aided Detection and Diagnosis of Diaphyseal Femur Fracture

Balaji, G.; Subashini, T. S.; Madhavi, P.; Bhavani, Ch.; Manikandarajan, A

doi:10.1007/978-981-13-9282-5_52

Cited by 9 publications

(6 citation statements)

References 9 publications

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“…The multidimensional graph cuts utilized by the proposed BKGC-CMSIS technique depends on the cost function determined from the classical graph cut [20] that reflects the characteristics of the initial shape of the nuclei and cytoplasm extracted from the pap smear cells. In this context, the process of segmentation [21] using multidimensional graph cuts estimated as the cost function minimization problem is expressed in Equation Where, E BT (D GC ) and E RT (D GC ) are the boundary term and regional term with ' κ ' as the balanced coefficient. Further, the cost function presented in Equation ( 1) that depends on shape constraints derived based on automatic graph construction is expressed in Equation ( 2)…”

Section: Methodsmentioning

confidence: 99%

Enhanced Boykov's graph cuts based segmentation for Cervical Cancer Detection

Balaji¹,

Suryanarayana²,

Sengathir³

2021

EAI Endorsed Trans Perv Health Tech

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Introduction: Cervical cancer is considered as the major curse to women society of the world due to their low survival rate. However, the prognosis of cervical cancer at an early stage through periodic screening was identified to enhance the survival probability of the women patients around the world. Objective: In this paper, a Boykov-Kolmogorov Graph Cuts and Cloud Model-based Synergy Integrated Segmentation (BKGC-CMSIS) Technique for facilitating predominant cervical cancer detection from the pap smear images used for prognosis. Methods: This proposed BKGC-CMSIS scheme introduces an effective Boykov-Kolmogorov Graph Cuts-based image partitioning method that estimates the image data through a synergy cloud model for formulating objective functions. The objective function used in this proposed BKGC-CMSIS scheme includes a data item and a smooth term for boundary preservation in order to determine the deviation of each pixel corresponding to the different regions of the cervical pap smear cells. Results: Also, it identifies the data item through the utilization of X-condition cloud generator for determining and defining the accurate boundaries of cytoplasm and nuclei derived from the pap smear cells. This proposed BKGC-CMSIS scheme uses the merits of membership degree through the incorporation of the smooth term for estimating the degree of similarity existing among the neighboring regions of the cervical pap smear cells. Conclusion: The experimental results of this proposed BKGC-CMSIS scheme is also potent in enhancing the classification accuracy by 14% superior to the benchmarked cervical cancer detection schemes considered for investigation.

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Section: Methodsmentioning

confidence: 99%

Enhanced Boykov's graph cuts based segmentation for Cervical Cancer Detection

Balaji¹,

Suryanarayana²,

Sengathir³

2021

EAI Endorsed Trans Perv Health Tech

View full text Add to dashboard Cite

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“…To begin, the original image's bone is obvious with second-hand to create dataset. Normalizing each layer's input, the hidden layer distributions of each layer may be assumed to be stable, allowing the goal of rapid training to be attained [ 6 , 7 ]. Computer-assisted diagnosis methods in orthopedic surgery have shown promise in mechanically identifying and detecting fractures.…”

Section: Related Workmentioning

confidence: 99%

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

Xiu

Rong

Zhang

2022

Computational and Mathematical Methods in Medicine

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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.

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“…The growth of DL in the healthcare systems is motivated by numerous features such as intelligent electronic medical records (EMR), intelligent [46][47][48] In order to avoid human errors in medical diagnosis system, the implementation ML or DL based automatic anomaly detection system is mandatory to increase the accuracy. In the era of DL based medical support systems, CNNs are the greatest asset, allowing multiple levels of abstractions to remove discerning characteristics.…”

Section: Human Bone Fracture Detection In DL Techniquesmentioning

confidence: 99%

“…CNN has the first stage of sampling function for initiating attribute learning process and classification functions. ML based clinical image processing framework consists of the phases such as region extraction, data registering, blending, footnote creation, and biodata generation 46–48 . In order to avoid human errors in medical diagnosis system, the implementation ML or DL based automatic anomaly detection system is mandatory to increase the accuracy.…”

Section: Human Bone Fracture Detection In DL Techniquesmentioning

confidence: 99%

A comparative experimental analysis and deep evaluation practices on human bone fracture detection using x‐ray images

Kumar

Prabu

Pandimurugan

et al. 2022

Concurrency and Computation

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Summary X‐ray images are widely used to identify fractures in human bones. Radiographic diagnosis models take more time and manual procedures for establishing physical analysis of bone fractures. Furthermore, the lack of clinical resources and the medical support systems lead to inaccurate bone data extractions. In this case, the need for detail is required to understand the scientific issues in x‐ray and magnetic resonance imaging (MRI) based bone fracture diagnosis solutions. Particularly, the current bone fracture detection models are emerging with computerized frameworks and health informatics consoles. In this regard, this article lists five phases. First, data preparation and data collection tasks are initiated. Second, bone fracture diagnosis models are comparatively analyzed to find optimal observations. Consequently, the third phase of the article examines the treatments against bone fracture observations with traditional and deep learning (DL) techniques. The fourth phase consists of relative diagnosis solutions and the treatment benefits on various types of technical frameworks. Finally, this work concludes the comparison with recent DL‐based bone fracture identification models.

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Computer-Aided Detection and Diagnosis of Diaphyseal Femur Fracture

Cited by 9 publications

References 9 publications

Enhanced Boykov's graph cuts based segmentation for Cervical Cancer Detection

Enhanced Boykov's graph cuts based segmentation for Cervical Cancer Detection

Construction of a Computer-Aided Analysis System for Orthopedic Diseases Based on High-Frequency Ultrasound Images

A comparative experimental analysis and deep evaluation practices on human bone fracture detection using x‐ray images

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