By artificial intelligence algorithms and machine learning models to diagnosis cancer

Agarwal, Seema; Yadav, Ajay Singh; Dinesh, V.; Vatsav, Kolluru Sai Sri; Prakash, K; Jaiswal, Sushma

doi:10.1016/j.matpr.2021.07.088

Cited by 21 publications

(9 citation statements)

References 12 publications

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“…AI may assist with collecting and evaluating data, diagnose the information on the basis of health, match it with prior information and expertise, and choose adequate diagnostic treatment plans. Thus, it has been studied for improving the diagnosis and management of many forms of cancer, including breast, lung, thyroid, oral, gastric, colorectal, liver, and skin cancers [44].…”

Section: Oncologymentioning

confidence: 99%

Clinical Applications of Artificial Intelligence—An Updated Overview

Busnatu

Niculescu

Bolocan

et al. 2022

JCM

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Artificial intelligence has the potential to revolutionize modern society in all its aspects. Encouraged by the variety and vast amount of data that can be gathered from patients (e.g., medical images, text, and electronic health records), researchers have recently increased their interest in developing AI solutions for clinical care. Moreover, a diverse repertoire of methods can be chosen towards creating performant models for use in medical applications, ranging from disease prediction, diagnosis, and prognosis to opting for the most appropriate treatment for an individual patient. In this respect, the present paper aims to review the advancements reported at the convergence of AI and clinical care. Thus, this work presents AI clinical applications in a comprehensive manner, discussing the recent literature studies classified according to medical specialties. In addition, the challenges and limitations hindering AI integration in the clinical setting are further pointed out.

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Section: Oncologymentioning

confidence: 99%

Clinical Applications of Artificial Intelligence—An Updated Overview

Busnatu

Niculescu

Bolocan

et al. 2022

JCM

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“…Images captured via computed tomography, magnetic resonance, ultrasound, endoscopy procedure, and biopsies aid in better prediction. Breast, lung, thyroid, gastric, oral, skin, and liver cancer are interpreted with principles of artificial intelligence and expert inputs [ 10 ]. The deep transfer neural network and extreme learning machine were integrated and experimented on over the lung image database consortium and image database resource initiative dataset, and accuracy of 94.94% was achieved [ 11 ].…”

Section: Related Workmentioning

confidence: 99%

A Transfer Learning Approach with a Convolutional Neural Network for the Classification of Lung Carcinoma

et al. 2022

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Lung cancer is among the most hazardous types of cancer in humans. The correct diagnosis of pathogenic lung disease is critical for medication. Traditionally, determining the pathological form of lung cancer involves an expensive and time-consuming process investigation. Lung cancer is a leading cause of mortality worldwide, with lung tissue nodules being the most prevalent way for doctors to identify it. The proposed model is based on robust deep-learning-based lung cancer detection and recognition. This study uses a deep neural network as an extraction of features approach in a computer-aided diagnosing (CAD) system to assist in detecting lung illnesses at high definition. The proposed model is categorized into three phases: first, data augmentation is performed, classification is then performed using the pretrained CNN model, and lastly, localization is completed. The amount of obtained data in medical image assessment is occasionally inadequate to train the learning network. We train the classifier using a technique known as transfer learning (TL) to solve the issue introduced into the process. The proposed methodology offers a non-invasive diagnostic tool for use in the clinical assessment that is effective. The proposed model has a lower number of parameters that are much smaller compared to the state-of-the-art models. We also examined the desired dataset’s robustness depending on its size. The standard performance metrics are used to assess the effectiveness of the proposed architecture. In this dataset, all TL techniques perform well, and VGG 16, VGG 19, and Xception for 20 epoch structure are compared. Preprocessing functions as a wonderful bridge to build a dependable model and eventually helps to forecast future scenarios by including the interface at a faster phase for any model. At the 20th epoch, the accuracy of VGG 16, VGG 19, and Xception is 98.83 percent, 98.05 percent, and 97.4 percent.

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“…Convolutional neural networks have been proposed to diagnose gastric endoscopy-based gastric cancer, and they performed better than human pathologists [25].…”

Section: Statistical Analysis and Machine Learning In Healthcarementioning

confidence: 99%

Choice of intraoperative ultrasound adjuncts for brain tumor surgery

Kumar

Noronha

Rangaraj

et al. 2022

BMC Med Inform Decis Mak

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Background Gliomas are among the most typical brain tumors tackled by neurosurgeons. During navigation for surgery of glioma brain tumors, preoperatively acquired static images may not be accurate due to shifts. Surgeons use intraoperative imaging technologies (2-Dimensional and navigated 3-Dimensional ultrasound) to assess and guide resections. This paper aims to precisely capture the importance of preoperative parameters to decide which type of ultrasound to be used for a particular surgery. Methods This paper proposes two bagging algorithms considering base classifier logistic regression and random forest. These algorithms are trained on different subsets of the original data set. The goodness of fit of Logistic regression-based bagging algorithms is established using hypothesis testing. Furthermore, the performance measures for random-forest-based bagging algorithms used are AUC under ROC and AUC under the precision-recall curve. We also present a composite model without compromising the explainability of the models. Results These models were trained on the data of 350 patients who have undergone brain surgery from 2015 to 2020. The hypothesis test shows that a single parameter is sufficient instead of all three dimensions related to the tumor ($$p < 0.05$$ p < 0.05 ). We observed that the choice of intraoperative ultrasound depends on the surgeon making a choice, and years of experience of the surgeon could be a surrogate for this dependence. Conclusion This study suggests that neurosurgeons may not need to focus on a large set of preoperative parameters in order to decide on ultrasound. Moreover, it personalizes the use of a particular ultrasound option in surgery. This approach could potentially lead to better resource management and help healthcare institutions improve their decisions to make the surgery more effective.

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By artificial intelligence algorithms and machine learning models to diagnosis cancer

Cited by 21 publications

References 12 publications

Clinical Applications of Artificial Intelligence—An Updated Overview

Clinical Applications of Artificial Intelligence—An Updated Overview

A Transfer Learning Approach with a Convolutional Neural Network for the Classification of Lung Carcinoma

Choice of intraoperative ultrasound adjuncts for brain tumor surgery

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