Efficient Local Cloud-Based Solution for Liver Cancer Detection Using Deep Learning

Anil, B C; P, Dayananda; Nethravathi, B; Raisinghani, Mahesh S.

doi:10.4018/ijcac.2022010109

Cited by 19 publications

(7 citation statements)

References 32 publications

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“…The combination of Mel-frequency cepstral coefficients (MFCC), fundamental frequency (F0), and spectral centroid has proven effective in identifying voice disorders due to their ability to capture complementary aspects of vocal quality. MFCCs capture spectral features that can reveal changes in vocal tract configuration 56 , 68 , while F0 reflects pitch variations related to vocal fold irregularities 69 , 70 . Spectral centroid, indicating spectral energy distribution, aids in detecting anomalies in voice production 71 , 72 .…”

Section: Resultsmentioning

confidence: 99%

“…Its specialized architecture with memory cells allows it to capture intricate dependencies within sequential data, making it a valuable choice for applications such as time series prediction and natural language processing. Artificial neural network (ANN): ANNs are versatile tools for classification tasks that are capable of processing diverse data types 56 – 58 . These networks consist of layers of interconnected nodes that adapt and learn from labeled training data 28 .…”

Section: Methodsmentioning

confidence: 99%

“…Artificial neural network (ANN): ANNs are versatile tools for classification tasks that are capable of processing diverse data types 56 – 58 . These networks consist of layers of interconnected nodes that adapt and learn from labeled training data 28 .…”

Section: Methodsmentioning

confidence: 99%

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A novel hybrid model integrating MFCC and acoustic parameters for voice disorder detection

Verma,

Benjwal,

Chhabra

et al. 2023

Sci Rep

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Voice is an essential component of human communication, serving as a fundamental medium for expressing thoughts, emotions, and ideas. Disruptions in vocal fold vibratory patterns can lead to voice disorders, which can have a profound impact on interpersonal interactions. Early detection of voice disorders is crucial for improving voice health and quality of life. This research proposes a novel methodology called VDDMFS [voice disorder detection using MFCC (Mel-frequency cepstral coefficients), fundamental frequency and spectral centroid] which combines an artificial neural network (ANN) trained on acoustic attributes and a long short-term memory (LSTM) model trained on MFCC attributes. Subsequently, the probabilities generated by both the ANN and LSTM models are stacked and used as input for XGBoost, which detects whether a voice is disordered or not, resulting in more accurate voice disorder detection. This approach achieved promising results, with an accuracy of 95.67%, sensitivity of 95.36%, specificity of 96.49% and f1 score of 96.9%, outperforming existing techniques.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A novel hybrid model integrating MFCC and acoustic parameters for voice disorder detection

Verma,

Benjwal,

Chhabra

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Along with the development of artificial intelligence, an increasing number of researchers are leveraging deep learning to address challenges in areas such as big data (Stergiou et al, 2021;Galiautdinov, 2021), health diagnosis (Shankar et al, 2021;Anil et al, 2022), fake information detection Tembhurne et al, 2022), and video understanding Xu et al, 2023). Video highlight detection (HD) is designed to output saliency scores for each video clip.…”

Section: Related Workmentioning

confidence: 99%

Query-Guided Refinement and Dynamic Spans Network for Video Highlight Detection and Temporal Grounding in Online Information Systems

Xu,

Sun,

Xie

et al. 2023

International Journal on Semantic Web and Information Systems

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With the surge in online video content, finding highlights and key video segments have garnered widespread attention. Given a textual query, video highlight detection (HD) and temporal grounding (TG) aim to predict frame-wise saliency scores from a video while concurrently locating all relevant spans. Despite recent progress in DETR-based works, these methods crudely fuse different inputs in the encoder, which limits effective cross-modal interaction. To solve this challenge, the authors design QD-Net (query-guided refinement and dynamic spans network) tailored for HD&TG. Specifically, they propose a query-guided refinement module to decouple the feature encoding from the interaction process. Furthermore, they present a dynamic span decoder that leverages learnable 2D spans as decoder queries, which accelerates training convergence for TG. On QVHighlights dataset, the proposed QD-Net achieves 61.87 HD-HIT@1 and 61.88 TG-mAP@0.5, yielding a significant improvement of +1.88 and +8.05, respectively, compared to the state-of-the-art method.

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“…At a deeper level, more complex and abstract feature information can be extracted, such as the semantic (category) information of the target. The excellent feature representation and learning ability of deep networks enable them to adapt to scene changes and have been successfully applied to remote sensing image segmentation tasks (Anil et al, 2022;Hasib et al, 2021). Therefore, a segmentation method based on a deep convolutional neural network and multiscale feature fusion was proposed.…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Semantic Segmentation Method Based on a Deep Convolutional Neural Network and Multiscale Feature Fusion

Zhang,

Jiang

2023

International Journal on Semantic Web and Information Systems

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There are many problems with remote sensing images, such as large data scales, complex illumination conditions, occlusion, and dense targets. The existing semantic segmentation methods for remote sensing images are not accurate enough for small and irregular target segmentation results, and the edge extraction results are poor. The authors propose a remote sensing image segmentation method based on a DCNN and multiscale feature fusion. Firstly, an end-to-end remote sensing image segmentation model using complete residual connection and multiscale feature fusion was designed based on a deep convolutional encoder–decoder network. Secondly, weighted high-level features were obtained using an attention mechanism, which better preserved the edges, texture, and other information of remote sensing images. The experimental results on ISPRS Potsdam and Urban Drone datasets show that compared with the comparison methods, this method has better segmentation effect on small and irregular objects and achieves the best segmentation performance while ensuring the computation speed.

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Efficient Local Cloud-Based Solution for Liver Cancer Detection Using Deep Learning

Cited by 19 publications

References 32 publications

A novel hybrid model integrating MFCC and acoustic parameters for voice disorder detection

A novel hybrid model integrating MFCC and acoustic parameters for voice disorder detection

Query-Guided Refinement and Dynamic Spans Network for Video Highlight Detection and Temporal Grounding in Online Information Systems

Remote Sensing Image Semantic Segmentation Method Based on a Deep Convolutional Neural Network and Multiscale Feature Fusion

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