Deep learning algorithms for brain disease detection with magnetic induction tomography

Abstract

In order to improve the reconstruction accuracy of magnetic induction tomography (MIT) and achieve fast imaging especially in the detection of cerebral hemorrhage, artificial intelligence algorithms are proposed to improve the accuracy of MIT inverse problem. Methods: According to the standard geometric data of human head, a three-dimensional (3D) head model containing four layer tissues is established for brain image reconstruction of MIT. Four deep learning (DL) networks, including restricted Boltzmann machine (RBM), deep belief network (DBN), stacked autoencoder (SAE), and denoising autoencoder (DAE), are used to solve the nonlinear reconstruction problem of MIT, and the reconstruction results of DL networks and back-projection algorithm are compared. Finally, in order to verify the practical value of DL algorithms, the phantom experiment is carried out with MIT detection system. Results: Using the nonlinear data learning ability of DL algorithms, the rapid and high-precision imaging of cerebral hemorrhage can be realized. Compared with the back-projection algorithm, the DL improves the artifact and the accuracy of the reconstruction image. The location and volume of bleeding can be reconstructed and the prediction time reaches 20 ms. Moreover, the anti-noise performance of the networks can reach 20 dB. Conclusions: The DL can effectively improve the reconstruction accuracy and prediction speed of the image when it is applied to the reconstruction of cerebral hemorrhage in MIT. This feasibility study MIT to be a potential technology for brain diseases to fully meet the needs of accurate, rapid, and low-cost clinical diagnosis and continuous monitoring.

Artificial Intelligence-Enabled Medical Analysis for Intracranial Cerebral Hemorrhage Detection and Classification

“…The experiments are carried out in external and internal cohorts consecutively, where it attains an enhanced lesion- and patient-level sensitivity. Chen et al [ 14 ] proposed an AI algorithm to enhance the performance of magnetic induction tomography (MIT) inverse problem. The DL systems, involving DAE, RBM, DBN, and SAE, are utilized for solving the nonlinear recreation problems of MIT and compared the recreation outcomes of DL networks.…”

Deep Learning Aided Neuroimaging and Brain Regulation

“…Classical machine learning methods such as support vector machine (SVM) or random forest require a well-prepared feature engineering procedure, in other words, need to manually segment morphological features and select import features, which is extremely time consuming and tends to show a large performance difference between different operators [4]. As AI techniques continue to be refined and improved, deep learning has been proposed to dramatically change the health care monitoring and regulation of the brain [5], which can not only improve the reconstruction accuracy of neuroimaging and achieve fast imaging, but also mine a large amount of pathological and genetic data by processing and cross-referencing health and medical big data such as images, pathology, and genes, and help pathologists to evaluate pathological sections faster to improve the efficiency and prognosis of disease diagnosis [6]. Deep learning is a special type of machine learning.…”

Vocal cord lesions classification based on deep convolutional neural network and transfer learning

“…Stemming from this subjective way, examination results vary from doctors with uneven experience, and cases may be misjudged. Fortunately, computer‐aided diagnosis (CAD) has the potential to boost accuracy and efficiency, providing the human expert with an objective complementary measure 6–9 …”

“…Fortunately, computer-aided diagnosis (CAD) has the potential to boost accuracy and efficiency, providing the human expert with an objective complementary measure. [6][7][8][9] Deep learning, particularly deep convolutional neural network (DCNN), has achieved promising results in many fields. It has obtained many encouraging progresses in auxiliary diagnoses, such as skin cancers detection based on dermoscopy images, 10 lung diseases diagnoses based on histopathology images, 11 diabetic retinopathy diagnosis based on retinal fundus images, 12 and bacteria identification based on fluorescence microscopy images.…”