Image Quality Assessment to Emulate Experts’ Perception in Lumbar MRI Using Machine Learning

Chabert, Stéren; Castro, Juan Sebastian; Muñoz, Leonardo; Cox, Pablo; Riveros, Rodrigo; Vielma, Juan; Huerta, Gamaliel; Querales, Marvin; Saavedra, Carolina; Veloz, Alejandro; Salas, Rodrigo

doi:10.3390/app11146616

Cited by 11 publications

(7 citation statements)

References 42 publications

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“…For further work, on the one hand, we intend to use data augmentation tools to include basophil images in the polymorphonuclear group in training and extend the model for the classification of immature leukocytes. On the other hand, it is also intended to develop machine learning techniques that include expert knowledge to improve performance [ 61 , 62 ].…”

Section: Discussionmentioning

confidence: 99%

An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

et al. 2022

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The evaluation of white blood cells is essential to assess the quality of the human immune system; however, the assessment of the blood smear depends on the pathologist’s expertise. Most machine learning tools make a one-level classification for white blood cell classification. This work presents a two-stage hybrid multi-level scheme that efficiently classifies four cell groups: lymphocytes and monocytes (mononuclear) and segmented neutrophils and eosinophils (polymorphonuclear). At the first level, a Faster R-CNN network is applied for the identification of the region of interest of white blood cells, together with the separation of mononuclear cells from polymorphonuclear cells. Once separated, two parallel convolutional neural networks with the MobileNet structure are used to recognize the subclasses in the second level. The results obtained using Monte Carlo cross-validation show that the proposed model has a performance metric of around 98.4% (accuracy, recall, precision, and F1-score). The proposed model represents a good alternative for computer-aided diagnosis (CAD) tools for supporting the pathologist in the clinical laboratory in assessing white blood cells from blood smear images.

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Section: Discussionmentioning

confidence: 99%

An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

et al. 2022

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“…When evaluating the set of 950 images, the authors achieve an accuracy score of 0.94 with their proposed three-step framework. Chabert et al (2021) [18] apply multiple methods (linear discriminant analysis, quadratic linear analysis, support vector machine, logistic regression, and multilayer perceptron) for assessment of Lumbar MRI quality. The best results are achieved with a combination of methods, with a recall of 0.82 and an AUC of 0.97.…”

Section: Related Workmentioning

confidence: 99%

“…= 0.99, Spec. = 0.95 [17] Ankle radiography 950 ACC = 0.94 [18] Lumbar MRI 95 R = 0.82, AUC = 0.77 [19] Retinopathy images 4000 AUC = 0.97, Sens. = 0.94, Spec.…”

Section: Ref Quality Assessment Typementioning

confidence: 99%

Quality Assessment Assistance of Lateral Knee X-rays: A Hybrid Convolutional Neural Network Approach

et al. 2023

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A common issue with X-ray examinations (XE) is the erroneous quality classification of the XE, implying that the process needs to be repeated, thus delaying the diagnostic assessment of the XE and increasing the amount of radiation the patient receives. The authors propose a system for automatic quality classification of XE based on convolutional neural networks (CNN) that would simplify this process and significantly decrease erroneous quality classification. The data used for CNN training consist of 4000 knee images obtained via radiography procedure (KXE) in total, with 2000 KXE labeled as acceptable and 2000 as unacceptable. Additionally, half of the KXE belonging to each label are right knees and left knees. Due to the sensitivity to image orientation of some CNNs, three approaches are discussed: (1) Left-right-knee (LRK) classifies XE based just on their label, without taking into consideration their orientation; (2) Orientation discriminator (OD) for the left knee (LK) and right knee (RK) analyses images based on their orientation and inserts them into two separate models regarding orientation; (3) Orientation discriminator combined with knee XRs flipped to the left or right (OD-LFK)/OD-RFK trains the models with all images being horizontally flipped to the same orientation and uses the aforementioned OD to determine whether the image needs to be flipped or not. All the approaches are tested with five CNNs (AlexNet, ResNet50, ResNet101, ResNet152, and Xception), with grid search and k-fold cross-validation. The best results are achieved using the OD-RFK hybrid approach with the Xception network architecture as the classifier and ResNet152 as the OD, with an average AUC of 0.97 (±0.01).

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“…Another investigation [29], used 75 lumbar images to develop a classifier method of the image quality perceived by radiologists and then obtain characteristics of the images to feed a classification algorithm and validate with another 20 images remaining from their data set. In their experiment, to classify the quality of the images, three neuroradiologists established the criteria for evaluating the image to classify it as good, poor, fair, reasonable, and excellent.…”

Section: Related Workmentioning

confidence: 99%

Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class

et al. 2021

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Breast cancer is one of the diseases of most profound concern, with the most prevalence worldwide, where early detections and diagnoses play the leading role against this disease achieved through imaging techniques such as mammography. Radiologists tend to have a high false positive rate for mammography diagnoses and an accuracy of around 82%. Currently, deep learning (DL) techniques have shown promising results in the early detection of breast cancer by generating computer-aided diagnosis (CAD) systems implementing convolutional neural networks (CNNs). This work focuses on applying, evaluating, and comparing the architectures: AlexNet, GoogLeNet, Resnet50, and Vgg19 to classify breast lesions after using transfer learning with fine-tuning and training the CNN with regions extracted from the MIAS and INbreast databases. We analyzed 14 classifiers, involving 4 classes as several researches have done it before, corresponding to benign and malignant microcalcifications and masses, and as our main contribution, we also added a 5th class for the normal tissue of the mammary parenchyma increasing the correct detection; in order to evaluate the architectures with a statistical analysis based on the received operational characteristics (ROC), the area under the curve (AUC), F1 Score, accuracy, precision, sensitivity, and specificity. We generate the best results with the CNN GoogLeNet trained with five classes on a balanced database with an AUC of 99.29%, F1 Score of 91.92%, the accuracy of 91.92%, precision of 92.15%, sensitivity of 91.70%, and specificity of 97.66%, concluding that GoogLeNet is optimal as a classifier in a CAD system to deal with breast cancer.

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Image Quality Assessment to Emulate Experts’ Perception in Lumbar MRI Using Machine Learning

Cited by 11 publications

References 42 publications

An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

Quality Assessment Assistance of Lateral Knee X-rays: A Hybrid Convolutional Neural Network Approach

Classification of Breast Cancer in Mammograms with Deep Learning Adding a Fifth Class

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