Segmentation of Nuclei in Histopathology images using Fully Convolutional Deep Neural Architecture

Natarajan, Vivek; Kumar, M. Sunil; Patan, Rizwan; Kallam, Suresh; Mohamed, Mohamed Yasin Noor

doi:10.1109/iccit-144147971.2020.9213817

Cited by 27 publications

(8 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the context of the Internet of Medical Things, Manimurugan et al utilized a deep belief neural network for effective attack detection in smart environments [13]. Furthermore, Natarajan et al applied fully convolutional deep neural architecture for the segmentation of nuclei in histopathology images [14]. Lastly, Selvaraj et al proposed an optimal virtual machine selection approach using swarm intelligence for anomaly detection [15].…”

Section: Related Workmentioning

confidence: 99%

Object Detection and Instance Segmentation with YOLOV8: Progress and Limitations

Lee,

Desa,

Azizan

et al. 2024

Proceedings of International Conference on Artificial Life and Robotics

View full text Add to dashboard Cite

This research employs object detection and instance segmentation algorithms to distinguish between objects and backgrounds and to interpret the detected objects. The YOLOV8 (You Only Look Once) framework and COCO dataset are utilized for detecting and interpreting the objects. Additionally, the accuracy of detection, segmentation, and interpretation is tested by placing objects at various distances from the camera. The algorithm's performance was evaluated, and the results were documented. In the experiments, a sample of 11 objects was tested, and 8 of them were successfully detected at distances of 45cm, 75cm, 105cm, and 135cm. For instance, segmentation, segmentation maps appeared clean when detecting a single object but faced challenges when multiple objects overlapped.

show abstract

Section: Related Workmentioning

confidence: 99%

Object Detection and Instance Segmentation with YOLOV8: Progress and Limitations

Lee,

Desa,

Azizan

et al. 2024

Proceedings of International Conference on Artificial Life and Robotics

View full text Add to dashboard Cite

show abstract

“…In order to assess the variations in image size, colour, background, angle, and location of interests, the method also requires experience in computer vision. The problems that afflict the hand crafted feature extraction can be successfully addressed by using deep learning models [22][36] [38].…”

Section: Related Workmentioning

confidence: 99%

Malaria cell detection using deep learning techniques and Investigation on efficacy and safety of carcia papaya leaf extract on malaria

2022

pnr

View full text Add to dashboard Cite

Malaria is a serious fever infection caused by Plasmodium parasites, which individuals get by bites from female Anopheles mosquitoes carrying the disease. 50% of the world's population impacted by malaria in 2020. People with HIV/AIDS, pregnant women, young children under 5, infants, and those moving to areas where malaria transmission is higher but immunity is lower, such as travellers, mobile communities, and migrant workers, are more likely to contract malaria and develop serious illnesses. Despite these figures, an early, accurate diagnosis can lower the fatality rate from malaria. Numerous factors, including a severe lack of experienced specialists in village areas, improper information management, the use of fake medications, the accessibility of affordable diagnostic tools, global warming, and many others, contribute to the development in malaria and their unnatural geographic location. Malaria is a complicated, fast spreading infectious illness that has become difficult to manage due to the prevalence of its parasites. It is difficult to distinguish between blood smear microscopic images of parasite-and non-parasite-infected blood due to malaria. We have created an automated solution using deep learning methods to address this difficulty. Additionally, the effectiveness and safety of papaya leaf extract from Carica papaya on malaria were examined.

show abstract

“…For the entire work, the paper presents the UNet model is adopted for the segmentation and classification of lung infection because the model is composed of encoding and decoding layers that preserve the spatial information among layers. [13] Compared to a fully connected network, the proposed model can localize and learn patterns more effectively.…”

Section: Feature Classificationmentioning

confidence: 99%

Lung Infection Detection using Progressive UNet Architecture

Pandey¹,

Dubey²

2022

APJHS

View full text Add to dashboard Cite

The fragmentation of medical images of tissue anomalies in organs or the blood vascular system is critical for any computerized diagnostic system. Nevertheless, automated segmentation in medical image analysis is complex since it requires in-depth information about the target organ architecture. This paper presents UNet, an end-to-end deep learning segmentation technique for early recognition of COVID. The proposed UNet model is progressive and capable of diagnosing different lung infection types along with COVID-19 infection. For this, computed tomography images are considered. The XGBoost classifier is integrated with UNet for feature classification in this model. The result analysis was performed on different convolution neural network models, that is, ResNet50, Inception, ResNet101, ResNet152, DenseNet, and UNet. These models were implemented on MATLAB using the deep neural network toolbox. From the result, it has been noticed that Inception achieved a minimum accuracy of 85.2%, and UNet achieved 99% of accuracy. It has been observed that UNet achieved approx. 16% of improvement over the inception model.

show abstract

Segmentation of Nuclei in Histopathology images using Fully Convolutional Deep Neural Architecture

Cited by 27 publications

References 13 publications

Object Detection and Instance Segmentation with YOLOV8: Progress and Limitations

Object Detection and Instance Segmentation with YOLOV8: Progress and Limitations

Malaria cell detection using deep learning techniques and Investigation on efficacy and safety of carcia papaya leaf extract on malaria

Lung Infection Detection using Progressive UNet Architecture

Contact Info

Product

Resources

About