Detection of Pulmonary Nodules Using Thresholding and Fractal Analysis

Ramesh, D; Jose, Deepa V.; Keerthana, R.; Krishnaveni, V.

doi:10.1007/978-3-319-71767-8_80

Cited by 19 publications

(3 citation statements)

References 12 publications

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“…Recently, video-based human face detection is broadly researched as it plays a vital role in video surveillance for detecting the face of criminals through the recorded videos in particular areas to solve several security issues. 26 This type of facial expression detection 27 • It increases the average FER rates.…”

Section: Proposed Model and Descriptionmentioning

confidence: 99%

“…Recently, video‐based human face detection is broadly researched as it plays a vital role in video surveillance for detecting the face of criminals through the recorded videos in particular areas to solve several security issues 26 . This type of facial expression detection 27 method is effective in defense and some security‐based fields because of its numerous identity measures like the depth of your eye sockets, the shape of your cheekbones, the distance between your eyes, ears, chin, the contour of the lips and the distance from forehead to the chin of the individuals. On the other hand, there are some drawbacks to facial expression detection while using the categorical methods that need to be focused on in the advanced state‐of‐the‐art methods.…”

Section: Proposed Face Expression Recognition Of Video Using Enhanced...mentioning

confidence: 99%

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Dynamic FERNet: Deep learning with optimal feature selection for face expression recognition in video

Jagadeesh

Baranidharan

2022

Concurrency and Computation

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This work concentrates on the recognition of facial emotion from video sequences with deep learning. Once the input video is converted into frames, the face detection is performed on each frame using the viola-jones face detection algorithm.Then, the feature extraction is performed by three well-performing feature extraction techniques like modified local directional pattern, spatio-temporal features, and scale-invariant feature transforms. The extracted features from all the frames of the video are concatenated. To reduce the feature-length for decreasing the training complexity, and enhance the recognition performance, the optimal feature selection is accomplished with the distance-based tunicate swarm algorithm. These selected features are processed to an innovative deep learning model termed a heuristically modified recurrent neural network. The same D-TSA improves the performance of RNN by optimally tuning its hidden neurons. Experimental results on a widely used benchmark dataset and manually collected dataset show that the classification performance is improved using spatio-temporal features, SIFT, M-LDP, and optimal feature selection, and thus, the proposed model with HM-RNN outperforms the other existing models. K E Y W O R D Sdistance-based tunicate swarm algorithm, face expression recognition in the video, heuristically modified recurrent neural network, Viola-Jones face detection algorithm INTRODUCTIONEmotions in human beings are understood by their facial expressions that are analyzed to identify the nature of that individual and their intentions. 1Hence, understanding these facial expressions is a persistent issue in the area of human-computer interaction (HCI). 2 Thus, these facial analyzes are broadly researched based on computer vision, in which certain applications like facial expression recognition and face recognition seek more attention in the improvement of network architectures. 3 Face expression recognition provides automated analysis in the practical applications for recognizing the facial expressions in videos, which influences the interaction interface between the human robots, which is the study of making communications among the humans and robots. Recently, the facial recognition approaches analyze the video frames using six basic expressions and also evaluated the neutral expression in the frames. 4 Similarly, face recognition methods are considered to be a significant biometric technique in many fields for providing authentication. 5 These methods use dynamic features like variations in facial texture and facial landmarks that provide eminent information for showing the emotional status. Thus, extracting the dynamic information from the whole video sequence is essential for recognizing the facial expression. These facial expression analysis systems are employed for categorizing the image or video sequence among the six basic expressions into single basic emotion. 6

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Section: Proposed Model and Descriptionmentioning

confidence: 99%

Section: Proposed Face Expression Recognition Of Video Using Enhanced...mentioning

confidence: 99%

Dynamic FERNet: Deep learning with optimal feature selection for face expression recognition in video

Jagadeesh

Baranidharan

2022

Concurrency and Computation

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“…Additionally, when the cervical images are dissimilar in appearance and irregular in shape, segmentation becomes difficult for these types of cervical images. To avoid these issues, deep learning is involved due to its ability to improve the performance of disease diagnosis in medical image processing which includes registration, identification, 31,32 segmentation, and classification. But, these deep learning techniques require a large amount of data for obtaining enhanced performance, particularly in positive cases.…”

Section: Yu Et Al 1 Cnnmentioning

confidence: 99%

Adaptive fuzzy‐region growing fusion and improved CNN‐ANFIS‐based automated segmentation and classification of cervical cancer

Jeyshri

Kowsigan

2022

Concurrency and Computation

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Summary The cervical cancer screening mechanisms help to classify cervical lesions into cancerous, precancerous, and normal. But, some of the classification approaches provide optimal outcomes based on the precise spotting and segmenting of cervical cancer. Moreover, still, challenges persist in the precise spotting of cervical cancer. The conventional screening methods for cervical cancer require more experienced pathologists, but it may also lead to poor efficiency and low accuracy. To overcome these issues, medical image processing introduces both machine learning and deep learning methods into cervical cell classification. The major scope of this task is to plan a new cervical cancer diagnosis method using intelligent approaches. Here, the segmentation of abnormality is done by the novel adaptive fuzzy‐region growing fusion model, which is improved by the hybridized optimization algorithm named gray wolf‐tunicate swarm algorithm (GW‐TSA). Then, the new ensemble classification termed Improved convolutional neural network with artificial neural network fuzzy inference system, which depends on the proposed GW‐TSA for performance enhancement through optimizing the hyperparameters. Finally, the simulation findings show that the designed approach is achievable and provides the highest accuracy rate in diagnosing cervical cancer.

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