Breast cancer is widespread around the world and can be cured if diagnosed at an early stage. Digital mammograms are used as the most effective imaging modalities for the diagnosis of breast cancer. ...However, mammography images suffer from low contrast, background noise as well as contrast as non-coherency among the regions, and these factors makes breast cancer diagnosis challenging. These problems can be overcome by using a new image enhancement technique. The objective of this research work is to enhance mammography images to improve the overall process of segmentation and classification of breast cancer diagnosis. We proposed the image enhancement for mammogram images, as well as the ablation of the pectoral muscle. The image enhancement technique involves several steps. In the first step, we process the mammography images in three channels (red, green and blue), the second step is based on the uniformity of the background on morphological operations, and the third step is to obtain a well-contrasted image using principal component analysis (PCA). The fourth step is based on the removal of the pectoral muscle using a seed-based region growth technique, and the last step contains the coherence of the different regions of the image using a second order Gaussian Laplacian (LoG) and an oriented diffusion filter to obtain a much-improved contrast image. The proposed image enhancement technique is tested with our data collected from different hospitals in Qassim health cluster Qassim province Saudi Arabia, and it contains the five Breast Imaging and Reporting System (BI-RADS) categories and this database contained 11,194 images (the images contain carnio-caudal (CC) view and mediolateral oblique(MLO) view of mammography images), and we used approximately 700 images to validate our database. We have achieved improved performance in terms of peak signal-to-noise ratio, contrast, and effective measurement of enhancement (EME) as well as our proposed image enhancement technique outperforms existing image enhancement methods. This performance of our proposed method demonstrates the ability to improve the diagnostic performance of the computerized breast cancer detection method.
The teeth are the most challenging material to work with in the human body. Existing methods for detecting teeth problems are characterised by low efficiency, the complexity of the experiential ...operation, and a higher level of user intervention. Older oral disease detection approaches were manual, time-consuming, and required a dentist to examine and evaluate the disease. To address these concerns, we propose a novel approach for detecting and classifying the four most common teeth problems: cavities, root canals, dental crowns, and broken-down root canals, based on the deep learning model. In this study, we apply the YOLOv3 deep learning model to develop an automated tool capable of diagnosing and classifying dental abnormalities, such as dental panoramic X-ray images (OPG). Due to the lack of dental disease datasets, we created the Dental X-rays dataset to detect and classify these diseases. The size of datasets used after augmentation was 1200 images. The dataset comprises dental panoramic images with dental disorders such as cavities, root canals, BDR, dental crowns, and so on. The dataset was divided into 70% training and 30% testing images. The trained model YOLOv3 was evaluated on test images after training. The experiments demonstrated that the proposed model achieved 99.33% accuracy and performed better than the existing state-of-the-art models in terms of accuracy and universality if we used our datasets on other models.
The Coronavirus disease 2019 (COVID-19) is an infectious disease spreading rapidly and uncontrollably throughout the world. The critical challenge is the rapid detection of Coronavirus infected ...people. The available techniques being utilized are body-temperature measurement, along with anterior nasal swab analysis. However, taking nasal swabs and lab testing are complex, intrusive, and require many resources. Furthermore, the lack of test kits to meet the exceeding cases is also a major limitation. The current challenge is to develop some technology to non-intrusively detect the suspected Coronavirus patients through Artificial Intelligence (AI) techniques such as deep learning (DL). Another challenge to conduct the research on this area is the difficulty of obtaining the dataset due to a limited number of patients giving their consent to participate in the research study. Looking at the efficacy of AI in healthcare systems, it is a great challenge for the researchers to develop an AI algorithm that can help health professionals and government officials automatically identify and isolate people with Coronavirus symptoms. Hence, this paper proposes a novel method CoVIRNet (COVID Inception-ResNet model), which utilizes the chest X-rays to diagnose the COVID-19 patients automatically. The proposed algorithm has different inception residual blocks that cater to information by using different depths feature maps at different scales, with the various layers. The features are concatenated at each proposed classification block, using the average-pooling layer, and concatenated features are passed to the fully connected layer. The efficient proposed deep-learning blocks used different regularization techniques to minimize the overfitting due to the small COVID-19 dataset. The multiscale features are extracted at different levels of the proposed deep-learning model and then embedded into various machine-learning models to validate the combination of deep-learning and machine-learning models. The proposed CoVIR-Net model achieved 95.7% accuracy, and the CoVIR-Net feature extractor with random-forest classifier produced 97.29% accuracy, which is the highest, as compared to existing state-of-the-art deep-learning methods. The proposed model would be an automatic solution for the assessment and classification of COVID-19. We predict that the proposed method will demonstrate an outstanding performance as compared to the state-of-the-art techniques being used currently.
Breast cancer is widespread worldwide and can be cured if diagnosed early. Using digital mammogram images and image processing with artificial intelligence can play an essential role in breast cancer ...diagnosis. As many computerized algorithms for breast cancer diagnosis have significant limitations, such as noise handling and varying or low contrast in the images, it can be difficult to segment the abnormal region. These challenges could be overcome by proposing a new pre-processing model, exploring its impact on the post-processing module, and testing it on an extensive database. In this research work, the three-step method is proposed and validated on large databases of mammography images. The first step corresponded to the database classification, followed by the second step, which removed the pectoral muscle from the mammogram image. The third stage utilized new image-enhancement techniques and a new segmentation module to detect abnormal regions in a well-enhanced image to diagnose breast cancer. The pre-and post-processing modules are based on novel image processing techniques. The proposed method was tested using data collected from different hospitals in the Qassim Health Cluster, Qassim Province, Saudi Arabia. This database contained the five categories in the Breast Imaging and Reporting and Data System and consisted of 2892 images; the proposed method is analyzed using the publicly available Mammographic Image Analysis Society database, which contained 322 images. The proposed method gives good contrast enhancement with peak-signal to noise ratio improvement of 3 dB. The proposed method provides an accuracy of approximately 92% on 2892 images of Qassim Health Cluster, Qassim Province, Saudi Arabia. The proposed method gives approximately 97% on the Mammographic Image Analysis Society database. The novelty of the proposed work is that it could work on all Breast Imaging and Reporting and Data System categories. The performance of the proposed method demonstrated its ability to improve the diagnostic performance of the computerized breast cancer detection method.
This study aimed to examine the validity and reproducibility of strain elastography (SE) for detecting prostate cancer (PCa) in patients with elevated prostate-specific antigen (PSA) levels. The ...study included 107 patients with elevated PSA levels. All eligible patients underwent transrectal ultrasound (TRUS) with real-time elastography (RTE) to detect suspicious lesions. Two readers independently evaluated the lesions and assigned a strain ratio and elastography score to each lesion. Histopathology was used as a reference standard to estimate the validity of RTE in predicting malignant lesions. An intraclass correlation (ICC) was performed to detect reliability of the strain ratios and elastography scores. TRUS-guided biopsy detected malignancies in 64 (59.8%) patients. TRUS with RTE revealed 122 lesions. The strain ratio index (SRI) cut-off values to diagnose malignancy were 4.05 and 4.35, with sensitivity, specificity, and accuracy of 94.7%, 91.3%, and 93.4%, respectively. An elastography score > 3 was the best cut-off value for detecting malignancy. According to readers, the sensitivity, specificity, and accuracy were 91.3-94.7%, 89.5-93.4%, and 91.3-90.9%, respectively. Excellent inter-reader agreement was recorded for SRI and elastography scores, with ICC of 0.937 and 0.800, respectively. SE proves to be an efficient tool for detecting PCa with high accuracy in patients with elevated PSA levels.
Background
There is limited data in the literature regarding the role of nonarthrographic MRI for detecting biceps pulley (BP) lesions.
Purpose
To assess the accuracy of nonarthrographic MRI for ...detecting BP lesions, and to evaluate the diagnostic value of various MRI signs (superior glenohumeral ligament discontinuity/nonvisibility, long head of biceps (LHB) displacement sign or subluxation/dislocation, LHB tendinopathy, and supraspinatus and subscapularis tendon lesions) in detecting such lesions.
Study Type
Retrospective.
Population
84 patients (32 in BP‐lesion group and 52 in BP‐intact group‐as confirmed by arthroscopy).
Field Strength/Sequence
1.5‐T, T1‐weighted turbo spin echo (TSE), T2‐weighted TSE, and proton density‐weighted TSE spectral attenuated inversion recovery (SPAIR) sequences.
Assessment
Three radiologists independently reviewed all MRI data for the presence of BP lesions and various MRI signs. The MRI signs and final MRI diagnoses were tested for accuracy regarding detecting BP lesions using arthroscopy results as the reference standard. Furthermore, the inter‐reader agreement (IRA) between radiologists was determined.
Statistical Tests
Student's t‐tests, Chi‐squared, and Fisher's exact tests, and 4‐fold table test were used. The IRA was calculated using Kappa statistics. A P‐value <0.05 was considered statistically significant.
Results
The sensitivity, specificity, and accuracy of nonarthrographic MRI for detecting BP lesions were 65.6%–78.1%, 90.4%–92.3%, and 81%–86.9%, respectively. The highest accuracy was noticed for the LHB displacement sign (84.5%–86.9%), and the highest sensitivity was registered for the LHB tendinopathy sign (87.5%). Furthermore, the highest specificity was observed for the LHB displacement sign and LHB subluxation/dislocation sign (98.1%–100%). The IRA regarding final MRI diagnosis and MRI signs of BP lesions was good to very good (κ = 0.76–0.98).
Data Conclusion
Nonarthrographic shoulder MRI may show good diagnostic accuracy for detecting BP lesions. The LHB displacement sign could serve as the most accurate and specific sign for diagnosis of BP lesions.
Level of Evidence
3
Technical Efficacy
Stage 2
In today’s world, a brain tumor is one of the most serious diseases. If it is detected at an advanced stage, it might lead to a very limited survival rate. Therefore, brain tumor classification is ...crucial for appropriate therapeutic planning to improve patient life quality. This research investigates a deep-feature-trained brain tumor detection and differentiation model using classical/linear machine learning classifiers (MLCs). In this study, transfer learning is used to obtain deep brain magnetic resonance imaging (MRI) scan features from a constructed convolutional neural network (CNN). First, multiple layers (19, 22, and 25) of isolated CNNs are constructed and trained to evaluate the performance. The developed CNN models are then utilized for training the multiple MLCs by extracting deep features via transfer learning. The available brain MRI datasets are employed to validate the proposed approach. The deep features of pre-trained models are also extracted to evaluate and compare their performance with the proposed approach. The proposed CNN deep-feature-trained support vector machine model yielded higher accuracy than other commonly used pre-trained deep-feature MLC training models. The presented approach detects and distinguishes brain tumors with 98% accuracy. It also has a good classification rate (97.2%) for an unknown dataset not used to train the model. Following extensive testing and analysis, the suggested technique might be helpful in assisting doctors in diagnosing brain tumors.
Brain tumors reduce life expectancy due to the lack of a cure. Moreover, their diagnosis involves complex and costly procedures such as magnetic resonance imaging (MRI) and lengthy, careful ...examination to determine their severity. However, the timely diagnosis of brain tumors in their early stages may save a patient’s life. Therefore, this work utilizes MRI with a machine learning approach to diagnose brain tumor severity (glioma, meningioma, no tumor, and pituitary) in a timely manner. MRI Gaussian and nonlinear scale features are extracted due to their robustness over rotation, scaling, and noise issues, which are common in image processing features such as texture, local binary patterns, histograms of oriented gradient, etc. For the features, each MRI is broken down into multiple small 8 × 8-pixel MR images to capture small details. To counter memory issues, the strongest features based on variance are selected and segmented into 400 Gaussian and 400 nonlinear scale features, and these features are hybridized against each MRI. Finally, classical machine learning classifiers are utilized to check the performance of the proposed hybrid feature vector. An available online brain MRI image dataset is utilized to validate the proposed approach. The results show that the support vector machine-trained model has the highest classification accuracy of 95.33%, with a low computational time. The results are also compared with the recent literature, which shows that the proposed model can be helpful for clinicians/doctors for the early diagnosis of brain tumors.
Digital mammography (DM) is the cornerstone of breast cancer detection. Digital breast tomosynthesis (DBT) is an advanced imaging technique used for diagnosing and screening breast lesions, ...particularly in dense breasts. This study aimed to evaluate the impact of combining DBT with DM on the BI-RADS categorization of equivocal breast lesions. We prospectively evaluated 148 females with equivocal BI-RADS breast lesions (BI-RADS 0, 3, and 4) with DM. All patients underwent DBT. Two experienced radiologists analyzed the lesions. They then assigned a BI-RADS category for each lesion according to the BI-RADS 2013 lexicon, using DM, DBT, and integrated DM and DBT. We compared the results based on major radiological characteristics, BI-RADS classification, and diagnostic accuracy, using the histopathological examination of the lesions as a reference standard. The total number of lesions was 178 on DBT and 159 on DM. Nineteen lesions were discovered using DBT and were missed by DM. The final diagnoses of 178 lesions were malignant (41.6%) and benign (58.4%). Compared to DM, DBT produced 34.8% downgrading and 32% upgrading of breast lesions. Compared with DM, DBT decreased the number of BI-RADS 4 and 3. All the upgraded BI-RADS 4 lesions were confirmed to be malignant. The combination of DM and DBT improves the diagnostic accuracy of BI-RADS for evaluating and characterizing mammographic equivocal breast lesions and allows for proper BI-RADS categorization.
Analyzing medical images has always been a challenging task because these images are used to observe complex internal structures of the human body. This research work is based on the study of the ...retinal fundus and magnetic resonance images (MRI) for the analysis of ocular and cerebral abnormalities. Typically, clinical quality images of the eyes and brain have low-varying contrast, making it challenge to diagnose a specific disease. These issues can be overcome, and preprocessing or an image enhancement technique is required to properly enhance images to facilitate postprocessing. In this paper, we propose an iterative algorithm based on the McCann Retinex algorithm for retinal and brain MRI. The foveal avascular zone (FAZ) region of retinal images and the coronal, axial, and sagittal brain images are enhanced during the preprocessing step. The High-Resolution Fundus (HRF) and MR brain Oasis images databases are used, and image contrast and peak signal-to-noise ratio (PSNR) are used to assess the enhancement step parameters. The average PSNR enhancement on images from the Oasis brain MRI database was about 3 dB with an average contrast of 57.4. The average PSNR enhancement of the HRF database images was approximately 2.5 dB with a contrast average of 40 over the database. The proposed method was also validated in the postprocessing steps to observe its impact. A well-segmented image was obtained with an accuracy of 0.953 and 0.0949 on the DRIVE and STARE databases. Brain tumors were detected from the Oasis brain MRI database with an accuracy of 0.97. This method can play an important role in helping medical experts diagnose eye diseases and brain tumors from retinal images and Oasis brain images.
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