Deep Learning Based Intelligent Classification Of Covid-19 &amp; Pneumonia Using Cough Auscultations

Naqvi, Syed Zohaib Hassan; Khan, Misha Urooj; Raza, Ali; Saeed, Zubair; Abbasi, Zeeshan; Ali, Syeda Zuriat-e-Zehra

doi:10.1109/imtic53841.2021.9719740

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…A deep learning model is usually a three-or more-layered neural network and is a type of AI [31,32]. These neural networks attempt, to mimic the functioning of the human brain by "learning" from vast amounts of data as done in [33,34]. Deep learning techniques are becoming increasingly popular in FDS.…”

Section: Deep Learning Based Fall Detectionmentioning

confidence: 99%

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

Ishtiaq

Saeed

Khan

et al. 2022

JAREE

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Falls are a major public health concern among the elderly and the number of gadgets designed to detect them has increased significantly in recent years. This document provides a detailed summary of research done on fall detection systems, with comparisons across different types of studies. Its purpose is to be a resource for doctors and engineers who are planning or conducting field research. Following the examination, datasets, limitations, and future imperatives in fall detection were discussed in detail. The quantity of research using context-aware approaches continues to rise, but there is a new trend toward integrating fall detection into smartphones, as well as the use of artificial intelligence in the detection algorithm. Concerns with real-world performance, usability, and reliability are also highlighted.

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Section: Deep Learning Based Fall Detectionmentioning

confidence: 99%

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

Ishtiaq

Saeed

Khan

et al. 2022

JAREE

View full text Add to dashboard Cite

show abstract

“…The emergence of artificial intelligence (AI) presents promising solutions in various applications [5][6][7][8] and especially for automating medical image analysis, thereby achieving faster and more accurate diagnoses [9]. Similarly, its sub-field, i.e., the DL techniques, has been widely used in radiology to extract robust features from MRI, PET, and CT scans.…”

Section: Introductionmentioning

confidence: 99%

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach

Saeed,

Bouhali,

et al. 2024

Bioengineering

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Brain cancer is a life-threatening disease requiring close attention. Early and accurate diagnosis using non-invasive medical imaging is critical for successful treatment and patient survival. However, manual diagnosis by radiologist experts is time-consuming and has limitations in processing large datasets efficiently. Therefore, efficient systems capable of analyzing vast amounts of medical data for early tumor detection are urgently needed. Deep learning (DL) with deep convolutional neural networks (DCNNs) emerges as a promising tool for understanding diseases like brain cancer through medical imaging modalities, especially MRI, which provides detailed soft tissue contrast for visualizing tumors and organs. DL techniques have become more and more popular in current research on brain tumor detection. Unlike traditional machine learning methods requiring manual feature extraction, DL models are adept at handling complex data like MRIs and excel in classification tasks, making them well-suited for medical image analysis applications. This study presents a novel Dual DCNN model that can accurately classify cancerous and non-cancerous MRI samples. Our Dual DCNN model uses two well-performed DL models, i.e., inceptionV3 and denseNet121. Features are extracted from these models by appending a global max pooling layer. The extracted features are then utilized to train the model with the addition of five fully connected layers and finally accurately classify MRI samples as cancerous or non-cancerous. The fully connected layers are retrained to learn the extracted features for better accuracy. The technique achieves 99%, 99%, 98%, and 99% of accuracy, precision, recall, and f1-scores, respectively. Furthermore, this study compares the Dual DCNN’s performance against various well-known DL models, including DenseNet121, InceptionV3, ResNet architectures, EfficientNetB2, SqueezeNet, VGG16, AlexNet, and LeNet-5, with different learning rates. This study indicates that our proposed approach outperforms these established models in terms of performance.

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“…The processed signals help users control their gait effectively. Ali et al (2023) proposes a deep learning-based Thought-to-Text conversion for patients with neurodegenerative diseases like Alzheimer’s disease type through EEG. Collected EEG signals are preprocessed with a band-pass filter and divided into five classifier tasks using XGBoost ( Ogunleye and Wang, 2019 ) classifier.…”

Section: Introductionmentioning

confidence: 99%

A non-contact interactive system for multimodal surgical robots based on LeapMotion and visual tags

Zhang,

Wang,

Dai

et al. 2023

Front. Neurosci.

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In recent years, the integration of robots in minimally invasive surgery has gained significant traction in clinical practice. However, conventional contact-based human-computer interaction poses the risk of bacterial infection, significantly limiting the role of robots in surgery. To address this limitation, we propose an innovative interaction method rooted in gestures and visual tags, allowing surgeons to control and fine-tune surgical robots without physical contact with the environment. By encoding the six gestures collected using LeapMotion, we can effectively control the surgical robot in a non-contact manner. Moreover, utilizing Aruco technology, we have accurately identified the 3D spatial position of the visual label, and developed 12 fine-tuning operations to refine surgical instruments. To evaluate the applicability of our proposed system in surgery, we designed a relevant experimental setup. In the experiment, we achieved enough precision. These results demonstrate that our system meets the clinical standard, providing doctors with a non-contact and flexible means of interacting with robots during surgery.

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Deep Learning Based Intelligent Classification Of Covid-19 & Pneumonia Using Cough Auscultations

Cited by 4 publications

References 17 publications

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

Fall Detection, Wearable Sensors & Artificial Intelligence: A Short Review

Cancerous and Non-Cancerous MRI Classification Using Dual DCNN Approach

A non-contact interactive system for multimodal surgical robots based on LeapMotion and visual tags

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