Use of Deep Learning for Disease Detection and Diagnosis

Mishra, Sushruta; Dash, Anuttam; Jena, Lambodar

doi:10.1007/978-981-15-5495-7_10

Cited by 41 publications

(5 citation statements)

References 8 publications

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“…Moreover, although DL technologies have shown a higher precision performance than ML technologies [66], it was found that the number of ML based approaches employing genetic variants only to predict AD were more than DL based approaches. In fact, only one research was found using SNPs data as the only modality to classify the disease.…”

Section: Discussionmentioning

confidence: 99%

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Alqubati¹,

Algaphari²

2021

IJSECS

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Alzheimer’s disease (AD) is a progressive neurodegenerative disorder. It can cause a massive impact on a patient's memory and mobility. As this disease is irreversible, early diagnosis is crucial for delaying the symptoms and adjusting the patient's lifestyle. Many machine learning (ML) and deep learning (DL) based-approaches have been proposed to accurately predict AD before its symptoms onset. However, finding the most effective approach for AD early prediction is still challenging. This review explored 24 papers published from 2018 until 2021. These papers have proposed different approaches using state of the art machine learning and deep learning algorithms on different biomarkers to early detect AD. The review explored them from different perspectives to derive potential research gaps and draw conclusions and recommendations. It classified these recent approaches in terms of the learning technique used and AD biomarkers. It summarized and compared their findings, and defined their strengths and limitations. It also provided a summary of the common AD biomarkers. From this review, it was found that some approaches strove to increase the prediction accuracy regardless of their complexity such as using heterogeneous datasets, while others sought to find the most practical and affordable ways to predict the disease and yet achieve good accuracy such as using audio data. It was also noticed that DL based-approaches with image biomarkers remarkably surpassed ML based-approaches. However, they achieved poorly with genetic variants data. Despite the great importance of genetic variants biomarkers, their large variance and complexity could lead to a complex approach or poor accuracy. These data are crucial to discover the underlying structure of AD and detect it at early stages. However, an effective pre-processing approach is still needed to refine these data and employ them efficiently using the powerful DL algorithms.

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Section: Discussionmentioning

confidence: 99%

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Alqubati¹,

Algaphari²

2021

IJSECS

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“…the training data set. Then it is used for classification, estimation and prediction of alert message decision [44][45][46][47].…”

Section: Plos Onementioning

confidence: 99%

Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment

et al. 2022

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Collaborative modelling of the Internet of Things (IoT) with Artificial Intelligence (AI) has merged into the Intelligence of Things concept. This recent trend enables sensors to track required parameters and store accumulated data in cloud storage, which can be further utilized by AI based predictive models for automatic decision making. In a smart and sustainable environment, effective waste management is a concern. Poor regulation of waste in surrounding areas leads to rapid spread of contagious disease risks. Traditional waste object management requires more working staff, increases effort, consumes time and is relatively ineffective. In this research, an Intelligence of Things Enabled Smart Waste Management (IoT-SWM) model with predictive capabilities is developed. Here, local sinks (LS) are deployed in specified locations. At every instant, the current status of smart bins in each LS is notified to users to determine the priority level of LS to be emptied. Based on aggregated sensor values for the three smart bins, LS weight and poison gas value, the priority order of emptying LS is computed, and decision is made whether to notify the users with an alert message or not. It also helps in predicting the LS, which is likely to be filled up at a faster rate based on assigned timestamp. This model is implemented in real time with many LS and it was observed that bins, which were close to more crowded sites filled up faster compared to sparse populated areas. Random forest algorithm was used to predict whether an alert notification is to be sent or not. An average mean of 95.8% accuracy was noted while using 60 decision trees in random forest algorithm. The average mean execution latency recorded for training and testing sets is 13.06 sec and 14.39 sec respectively. Observed accuracy rate, precision, recall and f1-score parameters were 95.8%, 96.5%, 98.5% and 97.2% respectively. Model buildup and the validation time computed were 3.26 sec and 4.25 sec respectively. It is also noted that at a threshold value of 0.93 in LS level, the maximum accuracy rate reached was 95.8%. Thus, based on the prediction of random forest approach, a decision to notify the users is taken. Obtained outcome indicates that the waste level can be efficiently determined, and the overflow of dustbins can be easily checked in time

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“…Deep learning uses the artificial neural networks to acquire features and patterns from huge datasets. In the area of medical imaging, deep learning algorithms have demonstrated promise for the identification and diagnosis of a number of diseases [6], including oral cancer [7]. Deep learning models [8] can assist in the early diagnosis of lesions or abnormalities by being trained to identify early indicators of mouth cancer in imaging data [9].…”

Section: Introductionmentioning

confidence: 99%

An improved deep convolutional neural network fororal cancer detection using pretrained models

2024

Preprint

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Purpose: Oral cancer is a type of cancer that arises from Oral Squamous Cell Carcinoma (OSCC) that develops in the mouth. Oral cancer is a major global public health issue, which emphasizes the urgent need in the research domain for targeted and effective approaches that enable timely identification. The current diagnostic approaches has a limitation of delay and inefficiency. Artificial Intelligence (AI) is nowadays extensively used the cancer diagnosis and prognosis can provide a fast results that helps in early detection of cancer and improves the survival rate of the cancer patients. Deep learning techniques offers several pretrained models in automating the cancer detection. The research focus on developing a light weight architecture with improved results and low computational costs based on DenseNet architecture of the Convolutional Neural Network (CNN). Methods: The number of dense blocks are reduced without compromising the classification of histopathology images as benign and malignant. The histopathology images are preprocessed in a way that is suitable to fed to a neural network. The hyper parameters of the model is fine tuned to obtained the better results. Results: The proposed model is evaluated with the standard performance metrics and compared with the other pretrained models. The proposed model provides 98.96% of classification accuracy in training and 82.49% in validation. The loss also has been reduced with a good precision of 0.98, recall of 0.76 and F1 score of 0.82. Conclusion: The proposed model also takes care of overfitting and vanishing gradient problem that could hamper the models performance. This will also help a medical practitioner can utilize these findings to formulate initial decisions and plan treatment for oral cancer.

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Use of Deep Learning for Disease Detection and Diagnosis

Cited by 41 publications

References 8 publications

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Machine learning and deep learning-based approaches on various biomarkers for Alzheimer’s disease early detection: A review

Prioritized and predictive intelligence of things enabled waste management model in smart and sustainable environment

An improved deep convolutional neural network fororal cancer detection using pretrained models

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