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- 저자 : Rama Mohan Babu. G (검색결과 6 건)
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An Optimized Deep Learning Techniques for Analyzing Mammograms
Satish Babu Bandaru,Natarajasivan. D,Rama Mohan Babu. G International Journal of Computer ScienceNetwork S 2023 International journal of computer science and netw Vol.23 No.7
Breast cancer screening makes extensive utilization of mammography. Even so, there has been a lot of debate with regards to this application's starting age as well as screening interval. The deep learning technique of transfer learning is employed for transferring the knowledge learnt from the source tasks to the target tasks. For the resolution of real-world problems, deep neural networks have demonstrated superior performance in comparison with the standard machine learning algorithms. The architecture of the deep neural networks has to be defined by taking into account the problem domain knowledge. Normally, this technique will consume a lot of time as well as computational resources. This work evaluated the efficacy of the deep learning neural network like Visual Geometry Group Network (VGG Net) Residual Network (Res Net), as well as inception network for classifying the mammograms. This work proposed optimization of ResNet with Teaching Learning Based Optimization (TLBO) algorithm's in order to predict breast cancers by means of mammogram images. The proposed TLBO-ResNet, an optimized ResNet with faster convergence ability when compared with other evolutionary methods for mammogram classification.
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Bandaru, Satish Babu,Babu, G. Rama Mohan International Journal of Computer ScienceNetwork S 2022 International journal of computer science and netw Vol.22 No.4
Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.
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B. Vikram Babu,M. Sushma Reddi,A. Rama Krishna,B. Sathish Mohan,G. Chandana,K. Anjani Devi,B. Sridhar,K. Samatha 한국전기전자재료학회 2022 Transactions on Electrical and Electronic Material Vol.23 No.5
This research article aims at reporting the influence of magnesium by studying the structural, electrical impedance and modulus properties of the Mg substituted Li 4 Ti 5 O 12 . These studies are useful for the electrochemical properties. The XRD reveals that the structure of all the Mg substituted materials belongs to the cubic spinel group having Fd-3m space symmetry. SEM images display the structural, morphological properties with the average size of grains falling in the vicinity of 1 μm. The electrical impedance of Li 4− x Mg x Ti 5 O 12 materials was analyzed at frequencies between 20 Hz and 1 MHz and in the 30–120 °C range of temperature by employing the complex impedance spectroscopy (CIS) method. The modulus formalism is also a suitable tool to understand the dynamical characteristics of electrical transport phenomena. The complex electric modulus spectrum signifi es quantifying the allocation of ion energies or confi gurations in the lattice. Also, it portrays the electrical relaxation of ion-conducting lattices as a feature of materials at a minuscule level. The obtained results of substitution of Mg in Li 4 Ti 5 O 12 anode materials improve the potential applications of conductivity and charge/discharge performance.
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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.
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