Ten Fast Transfer Learning Models for Carotid Ultrasound Plaque Tissue Characterization in Augmentation Framework Embedded with Heatmaps for Stroke Risk Stratification

Abstract

Background and Purpose: Only 1–2% of the internal carotid artery asymptomatic plaques are unstable as a result of >80% stenosis. Thus, unnecessary efforts can be saved if these plaques can be characterized and classified into symptomatic and asymptomatic using non-invasive B-mode ultrasound. Earlier plaque tissue characterization (PTC) methods were machine learning (ML)-based, which used hand-crafted features that yielded lower accuracy and unreliability. The proposed study shows the role of transfer learning (TL)-based deep learning models for PTC. Methods: As pertained weights were used in the supercomputer framework, we hypothesize that transfer learning (TL) provides improved performance compared with deep learning. We applied 11 kinds of artificial intelligence (AI) models, 10 of them were augmented and optimized using TL approaches—a class of Atheromatic™ 2.0 TL (AtheroPoint™, Roseville, CA, USA) that consisted of (i–ii) Visual Geometric Group-16, 19 (VGG16, 19); (iii) Inception V3 (IV3); (iv–v) DenseNet121, 169; (vi) XceptionNet; (vii) ResNet50; (viii) MobileNet; (ix) AlexNet; (x) SqueezeNet; and one DL-based (xi) SuriNet-derived from UNet. We benchmark 11 AI models against our earlier deep convolutional neural network (DCNN) model. Results: The best performing TL was MobileNet, with accuracy and area-under-the-curve (AUC) pairs of 96.10 ± 3% and 0.961 (p < 0.0001), respectively. In DL, DCNN was comparable to SuriNet, with an accuracy of 95.66% and 92.7 ± 5.66%, and an AUC of 0.956 (p < 0.0001) and 0.927 (p < 0.0001), respectively. We validated the performance of the AI architectures with established biomarkers such as greyscale median (GSM), fractal dimension (FD), higher-order spectra (HOS), and visual heatmaps. We benchmarked against previously developed Atheromatic™ 1.0 ML and showed an improvement of 12.9%. Conclusions: TL is a powerful AI tool for PTC into symptomatic and asymptomatic plaques.

COVLIAS 2.0-cXAI: Cloud-Based Explainable Deep Learning System for COVID-19 Lesion Localization in Computed Tomography Scans

“…Note that the unseen data (2450) was also not augmented. While several studies have been published that used the augmentation protocol [ 36 , 90 , 94 , 133 , 134 , 135 ] during classification, our DenseNet models for classification were never modified and never underwent change in rotation, tilt, or orientation. Further, note that we used the DICOM image directly, which contains orientation information.…”

Role of Artificial Intelligence in Radiogenomics for Cancers in the Era of Precision Medicine

“…(iii) Association of radiomics and genomics: In this step, both the radiomics features and genomics features of the cancer patients are combined to understand the tissue-level characterization of the cancerous regions or non-cancerous regions from the radiomics feature [ 51 , 52 , 53 ].…”

“…Similarly, various statistical tests, performance evaluation parameters, and performance analysis metrics have been involved in the data analysis of radiogenomics. The lesion localization analysis can be conducted by heatmap analysis for deep diagnosis [ 52 ].…”

Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models

“…In the extension of the work, we will make an effort for the advancement of the system, as it could be able to detect COVID-19 as well as the severity of the disease. In addition, we will include the heatmap images [127][128][129] of the disease, which will show the affected areas of the lungs. Broader advanced one-pass machine learning such as extreme learning machines [130] can be explored as more data are collected along with pruning methods [131][132][133] to lower the storage and improve the speed.…”