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Your search keyword '"Bhaskar, Nuthanakanti"' showing total 3 results
Start Over Author "Bhaskar, Nuthanakanti" Topic deep learning
3 results on '"Bhaskar, Nuthanakanti"'

1. Diabetic Retinopathy Detection Using Deep Learning

Author

Gothane, Suwarna, Raju, K. Srujan, Bhaskar, Nuthanakanti, Divya, G., Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Bhateja, Vikrant, editor, Khin Wee, Lai, editor, Lin, Jerry Chun-Wei, editor, Satapathy, Suresh Chandra, editor, and Rajesh, T. M., editor

Published
2022
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2. An approach for liver cancer detection from histopathology images using hybrid pre-trained models.

Author

Bhaskar, Nuthanakanti, Kiran, Jangala Sasi, Satyanarayan, Suma, Divya, Gaddam, Raju, Kotagiri Srujan, Kanthi, Murali, and Patra, Raj Kumar

Subjects
*DEEP learning, *LIVER cancer, *EARLY detection of cancer, *CANCER cell growth, *HISTOPATHOLOGY, *CONVOLUTIONAL neural networks
Abstract

Histopathological image analysis (HIA) plays an essential role in detecting cancer cell development, but it is time-consuming, prone to inaccuracy, and dependent on pathologist competence. This paper proposes an automated HIA that uses deep learning to improve accuracy and efficiency in liver cancer cell growth. The model uses whole slide image (WSI) input, open computer vision (OpenCV) libraries for image preprocessing, ResNet50 for patch-level feature extraction, and multiple instances learning for imagelevel classification. The suggested approach accurately distinguishes liver histopathological pictures as cancerous or non-cancerous. Assisting in the early detection of liver cancer cell development with potential invasion or spread. [ABSTRACT FROM AUTHOR]

Published
2024
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3. Detecting emotions in face using deep learning technique.

Author

Nishok, V. S., Sureshkumar, S., Prakash, A., Bhaskar, Nuthanakanti, Nandankar, Praful V., and Revathi, R.

Subjects
FACIAL expression & emotions (Psychology), EMOTION recognition, DEEP learning, ARTIFICIAL intelligence, CONVOLUTIONAL neural networks, EMOTIONAL state
Abstract

Identifying the Emotions of Human from the images are the most challenging research task. When it comes to detecting emotional states, deep learning-based algorithms outperform those that use visual processing. The creation of artificial intelligence (AI) system that can recognize emotions in facial expressions is presented in this research. There are three primary processes in the process of emotion detection: Detecting the faces, Extracting the features, and classification. This Deep learning architecture for emotion recognition from photos was presented in this article using convolutional neural networks (CNNs). The suggested approach is assessed based on the Facial Emotion Recognition Challenge and Japanese Female Facial Emotion dataset. There are 70.14% and 98.65% accuracy rates for the FERC-2013 and JAFFE datasets, respectively, with the suggested model. [ABSTRACT FROM AUTHOR]

Published
2023
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