Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.
Deep learning is an obvious method for the detection of disease, analyzing medical images and many researchers have looked into it. However, the performance of deep learning algorithms is frequently influenced by hyperparameter selection, the question of which combination of hyperparameters are best emerges. To address this challenge, we proposed a novel algorithm for Adaptive Hyperparameter Tuning (AHT) that automates the selection of optimal hyperparameters for Convolutional Neural Network (CNN) training. All of the optimal hyperparameters for the CNN models were instantaneously selected and allocated using a novel proposed algorithm Adaptive Hyperparameter Tuning (AHT). Using AHT, enables CNN models to be highly autonomous to choose optimal hyperparameters for classifying medical images into various classifications. The CNN model (Deep-Hist) categorizes medical images into basic classes: malignant and benign, with an accuracy of 95.71%. The most dominant CNN models such as ResNet, DenseNet, and MobileNetV2 are all compared to the already proposed CNN model (Deep-Hist). Plausible classification results were obtained using large, publicly available clinical datasets such as BreakHis, BraTS, NIH-Xray and COVID-19 X-ray. Medical practitioners and clinicians can utilize the CNN model to corroborate their first malignant and benign classification assessment. The recommended Adaptive high F1 score and precision, as well as its excellent generalization and accuracy, imply that it might be used to build a pathologist’s aid tool.
Estimating mitotic nuclei in breast cancer samples can aid in determining the tumor's aggressiveness and grading system. Because of their strong resemblance to non-mitotic nuclei and heteromorphic form, automated evaluation of mitotic nuclei is difficult. This study presents the BreastUNet, a new heteromorphous Deep Convolutional Neural Network (CNN) with feature grafting approach for analysing mitotic nuclei in breast histopathology images. In the first stage, the proposed method identifies probable mitotic patches in histopathological imaging regions, and in the second stage, the proposed model classifies these patches into mitotic and non-mitotic nuclei. For the building of a heteromorphous deep CNN, four distinct deep CNNs are developed and used as the basis CNN model. Deep CNNs with various architectural designs capture the structural, textural, and morphological aspects of mitotic nuclei. The performance of the proposed BreastUNet model is compared to those of state-of-the-art CNNs. The proposed model looks to be superior on the test set, with an F1 score of 0.95, Sensitivity and Specificity is 0.95 and area under the precision curve of 0.95. The recommended hybrid high F1 score and precision, as well as its excellent generalization and accuracy, imply that it might be used to build a pathologist's aid tool.
Classroom instructions have been an old method of teaching-learning process. In a typical lecture referring back to some established entity, idea or theory often leaves the student in a dificulty, yearning for the refresher of the basics.Essential time constraint, confinement of basic ideas and lack of interactive training aids make it cumbersome for students to cope up with new ideas being taught. Cyber Learning is relatively a new concept, involving on-line teacher-student interaction, information from related web-sites, and student-student chatting. Where the participants of diverse background can share different points of views on complex issues, coherent analysis and generates well-articulated and well-reasoned thoughts on core issues. Cyberspace interaction can surely help achieve the reformation, improvement and extension of quality education This paper presents the technologies, infrastructure and implementation of interactive campus as well as distance teaching techniques. Special emphasis will be on third world countries where economic constraint is the major hindrance in adopting technology. The on-line distance learning techniques, now in use worldwide, are presented together with a summary of their evaluation. The concept of ubiquitous computing on-campus and off-campus in the future, is the motif of this paper. This concept will be established by collecting real time statistical data of students, studying in different grades. The said concept involves more of the faculty contribution and setting up online and interactive lectures with the presence of Mentors, providing infrastructure to support the idea with emphasis on the supremacy of off-line Mentors and updated FAQs. The paper gives an over view of basic parameters and limitation of typical higher-education distance learning and teaching schemes. The benefits and limits of distance learning approach for the basic services, lessons, seminars and tutoring will be discussed. Finally, new concept of learning will be discussed as to how a student can learn interactively by being a student and a teacher
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