Performance Of Computer-aided Diagnosis System Research Articles

Diseases diagnosis based on artificial intelligence and ensemble classification

BackgroundIn recent years, Computer Aided Diagnosis (CAD) has become an important research area that attracted a lot of researchers. In medical diagnostic systems, several attempts have been made to build and enhance CAD applications to avoid errors that can cause dangerously misleading medical treatments. The most exciting opportunity for promoting the performance of CAD system can be accomplished by integrating Artificial Intelligence (AI) in medicine. This allows the effective automation of traditional manual workflow, which is slow, inaccurate and affected by human errors. AimsThis paper aims to provide a complete Computer Aided Disease Diagnosis (CAD2) strategy based on Machine Learning (ML) techniques that can help clinicians to make better medical decisions. MethodsThe proposed CAD2 consists of three main sequential phases, namely; (i) Outlier Rejection Phase (ORP), (ii) Feature Selection Phase (FSP), and (iii) Classification Phase (CP). ORP is implemented to reject outliers using new Outlier Rejection Technique (ORT) that contains two sequential stages called Fast Outlier Rejection (FOR) and Accurate Outlier Rejection (AOR). The most informative features are selected through FSP using Hybrid Selection Technique (HST). HST includes two main stages called Quick Selection Stage (QS2) using fisher score as a filter method and Precise Selection Stage (PS2) using a Hybrid Bio-inspired Optimization (HBO) technique as a wrapper method. Finally, actual diagnose takes place through CP, which relies on Ensemble Classification Technique (ECT). ResultsThe proposed CAD2 has been tested experimentally against recent disease diagnostic strategies using two different datasets in which the first contains several diseases, while the second includes data for Covid-19 patients only. Experimental results have proven the high efficiency of the proposed CAD2 in terms of accuracy, error, precision, and recall compared with other competitors. Additionally, CAD2 strategy provides the best Wilcoxon signed rank test and Friedman test measurements against other strategies according to both datasets. ConclusionIt is concluded that CAD2 strategy based on ORP, FSP, and CP gave an accurate diagnosis compared to other strategies because it gave the highest accuracy and the lowest error and implementation time.

Diagnostic performance of artificial intelligence-based computer-aided diagnosis system in longitudinal and transverse ultrasonic views for differentiating thyroid nodules.

To evaluate the diagnostic performance of different ultrasound sections of thyroid nodule (TN) using computer-aided diagnosis system based on artificial intelligence (AI-CADS) in predicting thyroid malignancy. This is a retrospective study. From January 2019 to July 2019, patients with preoperative thyroid ultrasound data and postoperative pathological results were enrolled, which were divided into two groups: lower risk group (ACR TI-RADS 1, 2 and 3) and higher risk group (ACR TI-RADS 4 and 5). The malignant risk scores (MRS) of TNs were obtained from longitudinal and transverse sections using AI-CADS. The diagnostic performance of AI-CADS and the consistency of each US characteristic were evaluated between these sections. The receiver operating characteristic (ROC) curve and the Cohen κ-statistic were performed. A total of 203 patients (45.61 ± 11.59 years, 163 female) with 221 TNs were enrolled. The area under the ROC curve (AUC) of criterion 3 [0.86 (95%CI: 0.80~0.91)] was lower than criterion 1 [0.94 (95%CI: 0.90~ 0.99)], 2 [0.93 (95%CI: 0.89~0.97)] and 4 [0.94 (95%CI: 0.90, 0.99)] significantly (P<0.001, P=0.01, P<0.001, respectively). In the higher risk group, the MRS of transverse section was higher than longitudinal section (P<0.001), and the agreement of extrathyroidal extension and shape was moderate and fair (κ =0.48, 0.31 respectively). The diagnostic agreement of other ultrasonic features was substantial or almost perfect (κ >0.60). The diagnostic performance of computer-aided diagnosis system based on artificial intelligence (AI-CADS) in longitudinal and transverse ultrasonic views for differentiating thyroid nodules (TN) was different, which was higher in the transverse section. It was more dependent on the section for the AI-CADS diagnosis of suspected malignant TNs.

Effect of situational and instrumental distortions on the classification of brain MR images

Magnetic Resonance (MR) images of the brain play key role in exploiting pathological changes and non-invasive investigation of many neuro-degenerative diseases. Computer Aided Diagnosis (CAD) systems assist radiologists in interpreting MR images and classifying them into “normal” and “abnormal” categories. However, reduced strength of the used magnet in the machine or involuntary motions of the patients may lead to degraded MR images, which can negatively affect the performance of CAD system compromising the classification accuracy. This work aims at modeling these types of situations via out-of-focus blur, motion blur, effect of variation in resolution, and a combination of these on brain MR images for validating the impact of image quality on classification performance. To validate this, this article mathematically models the blurs (both individually and simultaneously) by varying the strength of image quality covariates and afterwards Deep Convolutional Neural Networks (DCNN) are employed to train and classify the distorted brain MR images. Besides, a single DCNN is experimented with a good mix of image quality and characteristics to test the reliability of the model for real-life scenario. The CNN models are validated through comprehensive evaluation on both original and degraded versions of brain MR images from two benchmark datasets DS-75 and DS-160 collected by Harvard Medical School as well as a self-collected dataset NITR-DHH. This study reveals that the models are able to classify distorted MR images and hence can be used for assisting the clinicians.

Diagnosis of thyroid cancer using a TI-RADS-based computer-aided diagnosis system: a multicenter retrospective study

ObjectiveThe purpose of this study was to use a computer-aided diagnosis (CAD) system based on the Thyroid Imaging, Reporting, and Data System (TI-RADS) to improve the diagnostic performance of thyroid cancer by analyzing clinical ultrasound imaging data. MethodsA retrospective diagnostic study of ultrasound image sets was conducted at five hospitals in China. A CAD system based on TI-RADS was applied in this study, and the diagnostic performance of CAD system was tested through multi-center data. The performance of the CAD system was compared with the consensus of three experienced radiologists. The interobserver agreement for cancer diagnosis was calculated between the CAD system and the consensus of the three experienced radiologists. ResultsThe CAD system performed well in the diagnosis of thyroid cancer, with an area under the curve (AUC) value of 0.902 (95% CI: 0.884–0.918), and obtained results similar to those of the three experienced radiologists. The CAD system performed better in the internal test set than in the external test set (AUC: 0.930 vs 0.877, respectively). The performance of the CAD system in the diagnosis of thyroid cancer for nodules of different sizes (<1 cm, 1–2 cm and ≥2 cm) was basically similar (accuracy: 84.6% vs 85% vs 84.2%). The CAD system can recognize 15 ultrasound features of thyroid nodules, most of which reached the level of 3 experienced radiologists (12/15, 85%). ConclusionThe CAD system achieved an improved AUC and similar sensitivity and specificity in the diagnosis of thyroid cancer compared with the consensus of experienced radiologists.