Design Of Computer-aided Diagnosis System Research Articles

A CAD system design using iteratively reweighted fuzzy c-means and deep tree training for Alzheimer's disease diagnosis

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory loss and other cognitive abnormalities. In this research work, an MRI-based Computer-Aided Diagnosis (CAD) system is presented for clinical practice. While current algorithms used for detecting Alzheimer's disease perform inefficiently. Those algorithms are unable to detect minor changes in the disease due to discrepancies in feature extraction, segmentation, and classification methods. To overcome the issues in algorithm, a new iteratively reweighted adaptive spatial fuzzy c-means approach (IRW-ASFCM) is used to segment the brain tissue efficiently. Initially, the Magnetic resonance images (MRI) will pre-processed by using the Statistical parametric mapping (SPM) tool. After pre-processing, the segmentation is done using IRW-ASFCM to get effective tissue segments. Then, a 2-level selective Wavelet Kernel (2LS-WaveCNN ensemble) based CNN ensemble model will be used to extract the feature map from the Gray matter (GM), White matter (WM) and Cerebrospinal fluid (CSF) regions after segmenting the images. The Wave CNN networks built from hidden layers are trained using Deep Tree Training (DTT) for AD classification. The simulation results demonstrate that the suggested CAD system is superior to the state-of-the-art systems in terms of accuracy (99.54 %), sensitivity (99.26 %), and specificity (99.64 %) for multi-class problems.

Speckle Reduction via Deep Content-Aware Image Prior for Precise Breast Tumor Segmentation in an Ultrasound Image.

The performance of computer-aided diagnosis (CAD) systems that are based on ultrasound imaging has been enhanced owing to the advancement in deep learning. However, because of the inherent speckle noise in ultrasound images, the ambiguous boundaries of lesions deteriorate and are difficult to distinguish, resulting in the performance degradation of CAD. Although several methods have been proposed to reduce speckle noise over decades, this task remains a challenge that must be improved to enhance the performance of CAD. In this article, we propose a deep content-aware image prior (DCAIP) with a content-aware attention module (CAAM) for superior despeckling of ultrasound images without clean images. For the image prior, we developed a CAAM to deal with the content information in an input image. In this module, super-pixel pooling (SPP) is used to give attention to salient regions in an ultrasound image. Therefore, it can provide more content information regarding the input image when compared to other attention modules. The DCAIP consists of deep learning networks based on this attention module. The DCAIP is validated by applying it as a preprocessing step for breast tumor segmentation in ultrasound images, which is one of the tasks in CAD. Our method improved the segmentation performance by 15.89% in terms of the area under the precision-recall (PR) curve (AUPRC). The results demonstrate that our method enhances the quality of ultrasound images by effectively reducing speckle noise while preserving important information in the image, promising for the design of superior CAD systems.

A CAD system design for Alzheimer's disease diagnosis using temporally consistent clustering and hybrid deep learning models

Alzheimer Disease (AD) is a permanent brain syndrome that is triggered due to the dynamic deterioration of memory and cognitive functions. This paper presents a new computer-aided diagnosis (CAD) system to diagnose AD using Magnetic resonance imaging (MRI) data. Although the available AD diagnosis systems show good results, they did not capture the subtle changes of the disease due to inconsistencies in segmentation, feature extraction and classification processes. In the proposed CAD system, the tissues are initially segmented by proposing a new Temporally Consistent Black widow optimization (BWO) combined Fuzzy C-Means Clustering (FCM) clustering (TC-BW-FCM) segmentation method. It introduces temporal consistency constraints to address the temporal changes in intensity homogeneities by considering each tissue's bias field and intensity means. Also, a hybrid Texture, Edge, Color and density (TECD) feature extraction approach is combined with clinical data to give data about the emotional stage of the patient. A hybrid Rotation Forest Deep Neural Network (HRF-DNN) is proposed to improve the classification accuracy and used rotation forest to generate training feature subset for the Deep enhanced stacked auto encoder (DESAE) classifier. The simulation results show that the proposed CAD system outperforms the existing systems by increasing the accuracy, sensitivity, and specificity to 98.68%, 97.72% and 97.19%, respectively, for multi-class problems.

Ensemble of ROI-based convolutional neural network classifiers for staging the Alzheimer disease spectrum from magnetic resonance imaging.

Patches from three orthogonal views of selected cerebral regions can be utilized to learn convolutional neural network (CNN) models for staging the Alzheimer disease (AD) spectrum including preclinical AD, mild cognitive impairment due to AD, and dementia due to AD and normal controls. Hippocampi, amygdalae and insulae were selected from the volumetric analysis of structured magnetic resonance images (MRIs). Three-view patches (TVPs) from these regions were fed to the CNN for training. MRIs were classified with the SoftMax-normalized scores of individual model predictions on TVPs. The significance of each region of interest (ROI) for staging the AD spectrum was evaluated and reported. The results of the ensemble classifier are compared with state-of-the-art methods using the same evaluation metrics. Patch-based ROI ensembles provide comparable diagnostic performance for AD staging. In this work, TVP-based ROI analysis using a CNN provides informative landmarks in cerebral MRIs and may have significance in clinical studies and computer-aided diagnosis system design.

Rapid and efficient screening of human papillomavirus by Raman spectroscopy based on GA-SVM

In this study, a rapid, noninvasive and efficient screening method for human papillomavirus (HPV) was proposed by combining cervical secretion Raman spectral data with a pattern recognition algorithm. We measured the cervical secretion Raman spectra of 58 HPV positive and 196 HPV negative samples. The preliminary assignment of the Raman peaks in the measured cervical secretion spectra indicated changes in specific biomolecules between the groups, and we analyzed them according to their differences.The specificity of the airPLS-PLS-GA-SVM model was 100 %, the sensitivity was 94.4 %, and the total accuracy was 98.6 %. The results show that the combination of cervical secretion Raman spectroscopy and multivariate statistical analysis has great potential in the early screening of HPV and the samples of low-level lesions and high-level lesions were preliminarily classified effectively, providing a basis for the design of computer-aided diagnosis systems in the future.

Random Forests Based Group Importance Scores and Their Statistical Interpretation: Application for Alzheimer's Disease.

Machine learning approaches have been increasingly used in the neuroimaging field for the design of computer-aided diagnosis systems. In this paper, we focus on the ability of these methods to provide interpretable information about the brain regions that are the most informative about the disease or condition of interest. In particular, we investigate the benefit of group-based, instead of voxel-based, analyses in the context of Random Forests. Assuming a prior division of the voxels into non overlapping groups (defined by an atlas), we propose several procedures to derive group importances from individual voxel importances derived from Random Forests models. We then adapt several permutation schemes to turn group importance scores into more interpretable statistical scores that allow to determine the truly relevant groups in the importance rankings. The good behaviour of these methods is first assessed on artificial datasets. Then, they are applied on our own dataset of FDG-PET scans to identify the brain regions involved in the prognosis of Alzheimer's disease.

Hessian based approaches for 3D lung nodule segmentation

In the design of computer-aided diagnosis systems for lung cancer diagnosis, an appropriate and accurate segmentation of the pulmonary nodules in computerized tomography (CT) is one of the most relevant and difficult tasks. An accurate segmentation is crucial for the posterior measurement of nodule characteristics and for lung cancer diagnosis.This paper proposes different approaches that use Hessian-based strategies for lung nodule segmentation in chest CT scans. We propose a multiscale segmentation process that uses the central medialness adaptive principle, a Hessian-based strategy that was originally formulated for tubular extraction but it also provides good segmentation results in blob-like structures as is the case of lung nodules. We compared this proposal with a well established Hessian-based strategy that calculates the Shape Index (SI) and Curvedness (CV). We adapted the SI and CV approach for multiscale nodule segmentation. Moreover, we propose the combination of both strategies by combining the results, in order to take benefit of the advantages of both strategies.Different cases with pulmonary nodules from the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) database were taken and used to analyze and validate the approaches. The chest CT images present a large variability in nodule characteristics and image conditions. Our proposals provide an accurate lung nodule segmentation, similar to radiologists performance. Our Hessian-based approaches were validated with 569 solid and mostly solid nodules demonstrating that these novel strategies have good results when compared with the radiologists segmentations, providing accurate pulmonary nodule volumes for posterior characterization and appropriate diagnosis.

Diagnosis capability of ultrasound elastography and echography â feature extraction for CAD system

The design and development of computer aided diagnosis (CAD) systems is done based on the features extracted from medical images. It will be appropriate to combine the information obtained from different images for better diagnosis. Diagnosis capability of ultrasound elastography and echography are analysed using the images obtained from tissue mimicking phantoms and patient images. The results of this analysis can be used to select the feature set for the design of CAD system for the diagnosis of solid lesions. Ultrasound B mode and elastography images were obtained from the commercial available scanner. Parameters are extracted from the analysis of these images.