Breast Cancer Histopathological Image Classification Using Convolutional Neural Networks

Adhikari, Ankita; Choudhuri, Ashesh Roy; Ghosh, Dipak; Chattopadhyay, Neela; Chakraborty, Rupak

doi:10.1007/978-981-16-4301-9_14

Cited by 2 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditionally, we mainly rely on human visual observation or physical sensors for weather identification [5]; however, as human visual observation is generally subjective and infrequent, and observation times and locations are easily restricted by objective conditions, the final conclusions are also inaccurate.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A Study of Weather-Image Classification Combining VIT and a Dual Enhanced-Attention Module

Luo

2023

Electronics

View full text Add to dashboard Cite

A weather-image-classification model combining a VIT (vision transformer) and dual augmented attention module is proposed to address the problems of the insufficient feature-extraction capability of traditional deep-learning methods with the recognition accuracy still to be improved and the limited types of weather phenomena existing in the dataset. A pre-trained model vision transformer is used to acquire the basic semantic feature representation of weather images. Dual augmented attention combined with convolutional self-attention and Atrous self-attention modules are used to acquire the low-level and high-level deep-image semantic representations, respectively, and the feature vectors are spliced and fed into the linear layer to obtain the weather types. Experimental validation is performed on the publicly available standard weather-image datasets MWD (Multi-class Weather Database) and WEAPD (Weather Phenomenon Database), and the two datasets are combined to enhance the comprehensiveness of the model for weather-phenomena recognition. The results show that the model achieves the highest F1 scores of 97.47%, 87.69% and 92.73% on the MWD, WEAPD and merged datasets, respectively. These scores are higher than the scores of recent deep-learning models with excellent performance in the experimental comparison, thereby, proving the effectiveness of the model.

show abstract

Section: Related Workmentioning

confidence: 99%

“…Weather-image-classification methods based on feature engineering [5] usually have a complex tuning process and require strong expertise of the researcher. Each method is also application-specific, thus, resulting in poor model generalization ability as well as poor robustness.…”

Section: Introductionmentioning

confidence: 99%

A Study of Weather-Image Classification Combining VIT and a Dual Enhanced-Attention Module

Luo

2023

Electronics

View full text Add to dashboard Cite

show abstract

An Evaluation on the Performance of Privacy Preserving Split Neural Networks Using EMNIST Dataset

Vimaladevi¹,

Mathivanan²

2023

Communications in Computer and Information Science

View full text Add to dashboard Cite

Breast Cancer Histopathological Image Classification Using Convolutional Neural Networks

Cited by 2 publications

References 21 publications

A Study of Weather-Image Classification Combining VIT and a Dual Enhanced-Attention Module

A Study of Weather-Image Classification Combining VIT and a Dual Enhanced-Attention Module

An Evaluation on the Performance of Privacy Preserving Split Neural Networks Using EMNIST Dataset

Contact Info

Product

Resources

About