• The Journal of the Korea Contents Association
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• v.14 no.11
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• pp.362-369
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• 2014

This study aimed to propose an accurate diagnostic method for osteoporosis by realizing a computer-aided diagnosis system with the application of the statistical analysis of texture features using digital images of lateral lumbar spine of patients with osteoporosis and providing reliable supplementary diagnostic information by model experimental research for early diagnosis of diseases. For these purposes, digital images of lateral lumbar spine of normal individuals and patients with osteoporosis were used in the experiments, and the values of statistical texture features on the set ROI were expressed in six parameters. Among the texture feature values of the six parameters of osteoporosis, the highest and lowest recognition rates of 95 and 80% were shown in average gray level and uniformity, respectively. Moreover, all the six parameters showed recognition rates of over 80% for osteoporosis: 82.5% in average contrast, 90% in smoothness, 87.5% in skewness, and 87.5% in entropy. Therefore, if a program developing into a computer-aided diagnosis system for medical images is coded based on the results of this study, it is considered possible to be applied to preliminary diagnostic data for automatic detection of lesions and disease diagnosis using medical images, to provide information for definite diagnosis of diseases, to diagnose by limited device, and to be used to shorten the time to analyze medical images.

• Imaging Science in Dentistry
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• v.48 no.1
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• pp.45-49
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• 2018

Purpose: The aim of this study was to evaluate the quantitative strain elastography of tongue carcinoma using intraoral ultrasonography. Materials and Methods: Two patients with squamous cell carcinoma (SCC) who underwent quantitative strain elastography for the diagnosis of tongue lesions using intraoral ultrasonography were included in this prospective study. Strain elastography was performed using a linear 14 MHz transducer (Aplio 300; Canon Medical Systems, Otawara, Japan). Manual light compression and decompression of the tongue by the transducer was performed to achieve optimal and consistent color coding. The variation in tissue strain over time caused by the compression exerted using the probe was displayed as a strain graph. The integrated strain elastography software allowed the operator to place circular regions of interest (ROIs) of various diameters within the elastography window, and automatically displayed quantitative strain (%) for each ROI. Quantitative indices of the strain (%) were measured for normal tissues and lesions in the tongue. Results: The average strain of normal tissue and tongue SCC in a 50-year-old man was 1.468% and 0.000%, respectively. The average strain of normal tissue and tongue SCC in a 59-year-old man was 1.007% and 0.000%, respectively. Conclusion: We investigated the quantitative strain elastography of tongue carcinoma using intraoral ultrasonography. Strain elastography using intraoral ultrasonography is a promising technique for characterizing and differentiating normal tissues and SCC in the tongue.

• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.73-82
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• 2022

Early detection continues to be the mainstay of breast cancer control as well as the improvement of its treatment. Even so, the absence of cancer symptoms at the onset has early detection quite challenging. Therefore, various researchers continue to focus on cancer as a topic of health to try and make improvements from the perspectives of diagnosis, prevention, and treatment. This research's chief goal is development of a system with deep learning for classification of the breast cancer as non-malignant and malignant using mammogram images. The following two distinct approaches: the first one with the utilization of patches of the Region of Interest (ROI), and the second one with the utilization of the overall images is used. The proposed system is composed of the following two distinct stages: the pre-processing stage and the Convolution Neural Network (CNN) building stage. Of late, the use of meta-heuristic optimization algorithms has accomplished a lot of progress in resolving these problems. Teaching-Learning Based Optimization algorithm (TIBO) meta-heuristic was originally employed for resolving problems of continuous optimization. This work has offered the proposals of novel methods for training the Residual Network (ResNet) as well as the CNN based on the TLBO and the Genetic Algorithm (GA). The classification of breast cancer can be enhanced with direct application of the hybrid TLBO- GA. For this hybrid algorithm, the TLBO, i.e., a core component, will combine the following three distinct operators of the GA: coding, crossover, and mutation. In the TLBO, there is a representation of the optimization solutions as students. On the other hand, the hybrid TLBO-GA will have further division of the students as follows: the top students, the ordinary students, and the poor students. The experiments demonstrated that the proposed hybrid TLBO-GA is more effective than TLBO and GA.