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A Hybrid Optimized Deep Learning Techniques for Analyzing Mammograms
Bandaru, Satish Babu,Deivarajan, Natarajasivan,Gatram, Rama Mohan Babu International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.10
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.
딥러닝을 이용한 컨베이어 시스템의 배출구 막힘 상태 판단 기술에 관한 연구
정의한,서영주,김동주 한국컴퓨터정보학회 2020 韓國컴퓨터情報學會論文誌 Vol.25 No.5
This study proposes a technique for the determination of outlet blockage using deep learning in a conveyor system. The proposed method aims to apply the best model to the actual process, where we train various CNN models for the determination of outlet blockage using images collected by CCTV in an industrial scene. We used the well-known CNN model such as VGGNet, ResNet, DenseNet and NASNet, and used 18,000 images collected by CCTV for model training and performance evaluation. As a experiment result with various models, VGGNet showed the best performance with 99.03% accuracy and 29.05ms processing time, and we confirmed that VGGNet is suitable for the determination of outlet blockage. 본 연구는 컨베이어 시스템에서 딥러닝을 이용한 배출구 막힘 판단 기술에 대하여 제안한다. 제안 방법은 산업 현장의 CCTV에서 수집한 영상을 이용하여 배출구 막힘 판단을 위한 다양한CNN 모델들을 학습시키고, 성능이 가장 좋은 모델을 사용하여 실제 공정에 적용하는 것을 목적으로 한다. CNN 모델로는 잘 알려진 VGGNet, ResNet, DenseNet, 그리고 NASNet을 사용하였으며, 모델 학습과 성능 테스트를 위하여 CCTV에서 수집한 18,000장의 영상을 이용하였다. 다양한 모델에 대한 실험 결과, VGGNet은 99.89%의 정확도와 29.05ms의 처리 시간으로 가장 좋은 성능을 보였으며, 이로부터 배출구 막힘 판단 문제에 VGGNet이 가장 적합함을 확인하였다.