Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Tufail, Ahsan Bin; Ma, Yongkui; Kaabar, Mohammed K. A.; Martínez, Francisco; Junejo, A. R.; Ullah, Inam; Khan, Rahim

doi:10.1155/2021/9025470

Cited by 78 publications

(44 citation statements)

References 155 publications

(149 reference statements)

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“…e DL techniques have improved the area of computer engineering through various applicabilities, which are practically employed in every industry, from medical appliances to selfdriving cars. e deep neural network (DNN) models make use of the neural network architecture, which is why they are termed as deep neural networks [25][26][27]. ese models are trained on a large amount of labeled data and to extract features from it without the need for human intervention.…”

Section: Background and Existing Literaturementioning

confidence: 99%

An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health

Wahab

Zhao

Javeed

et al. 2022

Computational Intelligence and Neuroscience

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E-health has grown into a billion-dollar industry in the last decade. Its device’s high throughput makes it an obvious target for cyberattacks, and these environments desperately need protection. In this scientific study, we presented an artificial intelligence (AI)-driven software-defined networking (SDN)-enabled intrusion detection system (IDS) to address increasing cyber threats in the E-health and internet of medical things (IoMT) environments. AI’s success in various fields, including big data and intrusion detection systems, has prompted us to develop a flexible and cost-effective approach to protect such critical environments from cyberattacks. We present a hybrid model consisting of long short-term memory (LSTM) and gated recurrent unit (GRU). The proposed model was thoroughly evaluated using the publicly available CICDDoS2019 dataset and conventional evaluation measures. Furthermore, for proper validation, the proposed framework is compared with relevant classifiers, such as cu-GRU+ DNN and cu-BLSTM. We have further compared the proposed model with existing literature to prove its efficacy. Lastly, 10-fold cross-validation is also used to verify that our results are unbiased. The proposed approach has bypassed the current literature with extraordinary performance ramifications such as 99.01% accuracy, 99.04% precision, 98.80 percent recall, and 99.12% F1-score.

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Section: Background and Existing Literaturementioning

confidence: 99%

An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health

Wahab

Zhao

Javeed

et al. 2022

Computational Intelligence and Neuroscience

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“…Its performance is found to be better among other filtering approaches, and an interesting observation is that it never performed the worst in any of the classification tasks considered in this study. This study can be extended further by considering other deep learning approaches such as graph convolutional networks, as well as filtering methods such as those based on deep learning [59] (see also [60]).…”

Section: Discussionmentioning

confidence: 99%

Classification of Initial Stages of Alzheimer’s Disease through Pet Neuroimaging Modality and Deep Learning: Quantifying the Impact of Image Filtering Approaches

Tufail

Kaabar

et al. 2021

Mathematics

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Alzheimer’s disease (AD) is a leading health concern affecting the elderly population worldwide. It is defined by amyloid plaques, neurofibrillary tangles, and neuronal loss. Neuroimaging modalities such as positron emission tomography (PET) and magnetic resonance imaging are routinely used in clinical settings to monitor the alterations in the brain during the course of progression of AD. Deep learning techniques such as convolutional neural networks (CNNs) have found numerous applications in healthcare and other technologies. Together with neuroimaging modalities, they can be deployed in clinical settings to learn effective representations of data for different tasks such as classification, segmentation, detection, etc. Image filtering methods are instrumental in making images viable for image processing operations and have found numerous applications in image-processing-related tasks. In this work, we deployed 3D-CNNs to learn effective representations of PET modality data to quantify the impact of different image filtering approaches. We used box filtering, median filtering, Gaussian filtering, and modified Gaussian filtering approaches to preprocess the images and use them for classification using 3D-CNN architecture. Our findings suggest that these approaches are nearly equivalent and have no distinct advantage over one another. For the multiclass classification task between normal control (NC), mild cognitive impairment (MCI), and AD classes, the 3D-CNN architecture trained using Gaussian-filtered data performed the best. For binary classification between NC and MCI classes, the 3D-CNN architecture trained using median-filtered data performed the best, while, for binary classification between AD and MCI classes, the 3D-CNN architecture trained using modified Gaussian-filtered data performed the best. Finally, for binary classification between AD and NC classes, the 3D-CNN architecture trained using box-filtered data performed the best.

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“…Fortunately, the newest AI techniques can deal with the challenges that this complex and high-dimensional data poses. A wide variety of Machine Learning (ML), especially Deep Learning (DL) algorithms, have been used for this purpose with overall success [10,[19][20][21][22][23]. Indeed, in recent years the application of ML techniques to personalised medicine in order to enhance the accuracy of cancer progression and survival prediction has led to an improvement of 20-25% in the prediction of cancer prognosis [24].…”

Section: Introductionmentioning

confidence: 99%

Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis

Lobato-Delgado

Torres

Morillo

2022

Cancers

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Cancer is one of the most detrimental diseases globally. Accordingly, the prognosis prediction of cancer patients has become a field of interest. In this review, we have gathered 43 state-of-the-art scientific papers published in the last 6 years that built cancer prognosis predictive models using multimodal data. We have defined the multimodality of data as four main types: clinical, anatomopathological, molecular, and medical imaging; and we have expanded on the information that each modality provides. The 43 studies were divided into three categories based on the modelling approach taken, and their characteristics were further discussed together with current issues and future trends. Research in this area has evolved from survival analysis through statistical modelling using mainly clinical and anatomopathological data to the prediction of cancer prognosis through a multi-faceted data-driven approach by the integration of complex, multimodal, and high-dimensional data containing multi-omics and medical imaging information and by applying Machine Learning and, more recently, Deep Learning techniques. This review concludes that cancer prognosis predictive multimodal models are capable of better stratifying patients, which can improve clinical management and contribute to the implementation of personalised medicine as well as provide new and valuable knowledge on cancer biology and its progression.

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Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Cited by 78 publications

References 155 publications

An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health

An AI-Driven Hybrid Framework for Intrusion Detection in IoT-Enabled E-Health

Classification of Initial Stages of Alzheimer’s Disease through Pet Neuroimaging Modality and Deep Learning: Quantifying the Impact of Image Filtering Approaches

Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis

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