scholarly journals Transferable Architecture for Segmenting Maxillary Sinuses on Texture-Enhanced Occipitomental View Radiographs

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 768
Author(s):  
Peter Chondro ◽  
Qazi Mazhar ul Haq ◽  
Shanq-Jang Ruan ◽  
Lieber Po-Hung Li
Keyword(s):  
Morphological Analysis ◽  
Automatic Segmentation ◽  
The Novel ◽  
Computer Aided Detection ◽  
Convolutional Network ◽  
Segmentation Accuracy ◽  
Computer Aided ◽  
Skull Radiography ◽  
Aided Diagnosis ◽  
Maxillary Sinuses

Maxillary sinuses are the most prevalent locations for paranasal infections on both children and adults. Common diagnostic material for this particular disease is through the screening of occipitomental-view skull radiography (SXR). With the growing cases on paranasal infections, expediting the diagnosis has become an important innovation aspect that could be addressed through the development of a computer-aided diagnosis system. As the preliminary stage of the development, an automatic segmentation over the maxillary sinuses is required to be developed. This study presents a computer-aided detection (CAD) module that segments maxillary sinuses from a plain SXR that has been preprocessed through the novel texture-based morphological analysis (ToMA). Later, the network model from the Transferable Fully Convolutional Network (T-FCN) performs pixel-wise segmentation of the maxillary sinuses. T-FCN is designed to be trained with multiple learning stages, which enables re-utilization of network weights to be adjusted based on newer dataset. According to the experiments, the proposed system achieved segmentation accuracy at 85.70%, with 50% faster learning time.

Overview of Computer Aided Detection and Computer Aided Diagnosis Systems for Lung Nodule Detection in Computed Tomography

2020 ◽  
Vol 16 (1) ◽  
pp. 16-26 ◽  
Author(s):  
Shabana Rasheed Ziyad ◽  
Venkatachalam Radha ◽  
Thavavel Vayyapuri
Keyword(s):  
Computed Tomography ◽  
Review Paper ◽  
Lung Nodule ◽  
Lung Nodules ◽  
Computer Aided Detection ◽  
Lung Nodule Detection ◽  
Nodule Detection ◽  
Computer Aided ◽  
Aided Diagnosis

Background: Lung cancer has become a major cause of cancer-related deaths. Detection of potentially malignant lung nodules is essential for the early diagnosis and clinical management of lung cancer. In clinical practice, the interpretation of Computed Tomography (CT) images is challenging for radiologists due to a large number of cases. There is a high rate of false positives in the manual findings. Computer aided detection system (CAD) and computer aided diagnosis systems (CADx) enhance the radiologists in accurately delineating the lung nodules. Objectives: The objective is to analyze CAD and CADx systems for lung nodule detection. It is necessary to review the various techniques followed in CAD and CADx systems proposed and implemented by various research persons. This study aims at analyzing the recent application of various concepts in computer science to each stage of CAD and CADx. Methods: This review paper is special in its own kind because it analyses the various techniques proposed by different eminent researchers in noise removal, contrast enhancement, thorax removal, lung segmentation, bone suppression, segmentation of trachea, classification of nodule and nonnodule and final classification of benign and malignant nodules. Results: A comparison of the performance of different techniques implemented by various researchers for the classification of nodule and non-nodule has been tabulated in the paper. Conclusion: The findings of this review paper will definitely prove to be useful to the research community working on automation of lung nodule detection.

An approach for automatic segmentation of thermal imaging in Computer Aided Diagnosis

2016 ◽  
Vol 14 (4) ◽  
pp. 1856-1865 ◽  
Author(s):  
Rafael Souza Marques ◽  
Aura Conci ◽  
Maria G. Perez ◽  
Victor H. Andaluz ◽  
Tatiana M. Mejia
Keyword(s):  
Thermal Imaging ◽  
Automatic Segmentation ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

Directional Vector-Based Skin Lesion Segmentation — A Novel Approach to Skin Segmentation

2020 ◽  
Vol 20 (03) ◽  
pp. 2050021
Author(s):  
P. Nikesh ◽  
G. Raju
Keyword(s):  
Skin Lesion ◽  
Automatic Segmentation ◽  
High Accuracy ◽  
Lesion Segmentation ◽  
Seed Point ◽  
Skin Segmentation ◽  
Novel Approach ◽  
Segmentation Algorithms ◽  
Computer Aided ◽  
Aided Diagnosis

Efficient skin lesion segmentation algorithms are required for computer aided diagnosis of skin cancer. Several algorithms were proposed for skin lesion segmentation. The existing algorithms are short of achieving ideal performance. In this paper, a novel semi-automatic segmentation algorithm is proposed. The fare concept of the proposed is 8-directional search based on threshold for lesion pixel, starting from a user provided seed point. The proposed approach is tested on 200 images from PH2 and 900 images from ISBI 2016 datasets. In comparison to a chosen set of algorithms, the proposed approach gives high accuracy and specificity values. A significant advantage of the proposed method is the ability to deal with discontinuities in the lesion.

Polypendetektion mit künstlicher Intelligenz

Endo-Praxis ◽  
2021 ◽  
Vol 37 (01) ◽  
pp. 37-42
Author(s):  
Andres Rademacher ◽  
Siegbert Faiss
Keyword(s):  
Deep Learning ◽  
Detection Rate ◽  
Computer Aided Diagnosis ◽  
Adenoma Detection Rate ◽  
Computer Aided Detection ◽  
Adenoma Detection ◽  
Computer Aided ◽  
Aided Diagnosis

ZusammenfassungDurch die Vorsorgekoloskopie lässt sich die Inzidenz und die Sterblichkeit des kolorektalen Karzinoms effektiv senken. Die Adenomdetektionsrate (ADR = engl. adenoma detection rate) stellt ein entscheidendes Qualitätskriterium der Vorsorgekoloskopie dar. Die Nutzung computerbasierender Assistenzsysteme in der Endoskopie bietet große Chancen, die Adenomdetektionsrate weiter zu steigern und für eine weitere Qualitätssicherung in der Endoskopie zu sorgen.Die theoretischen Grundlagen der künstlichen Intelligenz wurden bereits in den 1950er-Jahren gelegt, eine breite Anwendung ist jedoch erst jetzt durch die Entwicklung schneller Computer und die Verfügbarkeit großer digitaler Datenmengen möglich. Das Deep Learning (dt. mehrschichtiges Lernen oder tiefes Lernen) stellt eine Form des maschinellen Lernens dar, bei dem durch Nutzung eines künstlichen neuronalen Netzwerks nach einer Lernphase komplexe Aufgaben gelöst werden können. Es eignet sich für Anwendungen, die für das menschliche Gehirn keine große Anstrengung darstellen (wie z. B. Gesichts- oder Spracherkennung), die jedoch mit konventionellen Methoden sehr aufwendig zu programmieren sind.Für den Einsatz in der Endoskopie wurden auf künstlicher Intelligenz basierende Systeme zur computergestützten Polypendetektion (engl. computer aided Detection = CADe), computergestützte Diagnose (engl. computer aided diagnosis = CADx) und zum computergestützten Monitoring (engl. computer aided monitoring = CADm) erfolgreich in Studien getestet. Erste kommerzielle Systeme zur Polypendetektion und zur optischen Biopsie im Kolon sind bereits erhältlich und konnten in Studien eine Steigerung der ADR durch Einsatz der künstlichen Intelligenz belegen.Computergestützte Assistenzsysteme auf Basis des Deep Learning könnten in naher Zukunft zum Standard in der Endoskopie werden, um eine optimale Polypendetektion, akkurate Diagnosestellung und objektives Untersuchungsmonitoring zu gewährleisten.

scholarly journals NanoChest-Net: A Simple Convolutional Network for Radiological Studies Classification

Diagnostics ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 775
Author(s):  
Juan Eduardo Luján-García ◽  
Yenny Villuendas-Rey ◽  
Itzamá López-Yáñez ◽  
Oscar Camacho-Nieto ◽  
Cornelio Yáñez-Márquez
Keyword(s):  
Breast Cancer ◽  
State Of The Art ◽  
X Rays ◽  
Huge Amount ◽  
Convolutional Network ◽  
X Ray ◽  
Learning Techniques ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Radiological Studies

The new coronavirus disease (COVID-19), pneumonia, tuberculosis, and breast cancer have one thing in common: these diseases can be diagnosed using radiological studies such as X-rays images. With radiological studies and technology, computer-aided diagnosis (CAD) results in a very useful technique to analyze and detect abnormalities using the images generated by X-ray machines. Some deep-learning techniques such as a convolutional neural network (CNN) can help physicians to obtain an effective pre-diagnosis. However, popular CNNs are enormous models and need a huge amount of data to obtain good results. In this paper, we introduce NanoChest-net, which is a small but effective CNN model that can be used to classify among different diseases using images from radiological studies. NanoChest-net proves to be effective in classifying among different diseases such as tuberculosis, pneumonia, and COVID-19. In two of the five datasets used in the experiments, NanoChest-net obtained the best results, while on the remaining datasets our model proved to be as good as baseline models from the state of the art such as the ResNet50, Xception, and DenseNet121. In addition, NanoChest-net is useful to classify radiological studies on the same level as state-of-the-art algorithms with the advantage that it does not require a large number of operations.

scholarly journals Fast Deep Learning Computer-Aided Diagnosis against the Novel COVID-19 pandemic from Digital Chest X-ray Images

2020 ◽  
Author(s):  
Mugahed A. Al-antari ◽  
Cam-Hao Hua ◽  
Sungyoung Lee
Keyword(s):  
Deep Learning ◽  
Lung Disease ◽  
Lung Diseases ◽  
Diagnostic Performance ◽  
The Novel ◽  
X Ray ◽  
Cad System ◽  
Computer Aided ◽  
Chest X Ray ◽  
Aided Diagnosis

Abstract Background and Objective: The novel coronavirus 2019 (COVID-19) is a harmful lung disease that rapidly attacks people worldwide. At the end of 2019, COVID-19 was discovered as mysterious lung disease in Wuhan, Hubei province of China. World health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 from the entire digital X-ray images is the key to efficiently assist patients and physicians for a fast and accurate diagnosis.Methods: In this paper, a deep learning computer-aided diagnosis (CAD) based on the YOLO predictor is proposed to simultaneously detect and diagnose COVID-19 among the other eight lung diseases: Atelectasis, Infiltration, Pneumothorax, Mass, Effusion, Pneumonia, Cardiomegaly, and Nodule. The proposed CAD system is assessed via five-fold tests for multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system is trained using an annotated training set of 50,490 chest X-ray images.Results: The suspicious regions of COVID-19 from the entire X-ray images are simultaneously detected and classified end-to-end via the proposed CAD predictor achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. The most testing images of COVID-19 and other lunge diseases are correctly predicted achieving intersection over union (IoU) with their GTs greater than 90%. Applying deep learning regularizers of data balancing and augmentation improve the diagnostic performance by 6.64% and 12.17% in terms of overall accuracy and F1-score, respectively. Meanwhile, the proposed CAD system presents its feasibility to diagnose the individual chest X-ray image within 0.009 second. Thus, the presented CAD system could predict 108 frames/second (FPS) at the real-time of prediction.Conclusion: The proposed deep learning CAD system shows its capability and reliability to achieve promising COVID-19 diagnostic performance among all other lung diseases. The proposed deep learning model seems reliable to assist health care systems, patients, and physicians in their practical validations.

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