scholarly journals Radiomics & Radiology: A Critical Step towards Integrated Healthcare

2020 ◽  
Vol 8 (2) ◽  
pp. 23-30
Author(s):  
Mayur Pankhania ◽  
Aditya Mehta
Keyword(s):  
Paradigm Shift ◽  
High Performance ◽  
Swot Analysis ◽  
Great Promise ◽  
Computer Aided Detection ◽  
Integrated Healthcare ◽  
Higher Dimensional ◽  
Computer Aided ◽  
Technical Issues ◽  
Aided Diagnosis

Radiomics have shown great promise for integrated healthcare. Radiomics is defined as high-performance retrieval of significant volumes of characteristics from images and conversion of images to higher-dimensional data and subsequently mining for improved support for therapeutic judgements. It has its roots within Computer-Aided Detection (CAD)or Computer-Aided Diagnosis (CADx); it is unique in many aspects. It does not just detect and diagnose but also ventures into therapeutic, prediction, projection and modelling that can be used to generalize and reproduced. It has great potential in creating a paradigm shift in the way healthcare is delivered and perceived. We will review and outline the stage of radiomics& its SWOT analysis, exclusively addressing application in medical imaging and spotlighting the technical issues.

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.

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 Detection and Classification of Leukocytes in Leukemia using YOLOv2 with CNN

2021 ◽  
pp. 64-75
Author(s):  
Shakir M. Abas ◽  
Adnan M. Abdulazeez
Keyword(s):  
Blood Cells ◽  
High Performance ◽  
White Blood Cells ◽  
Computer Aided Diagnosis ◽  
Main Task ◽  
Average Precision ◽  
Leukemia Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis

The development of machine learning systems that used for diagnosis of chronic diseases is challenging mainly due to lack of data and difficulty of diagnosing. This paper compared between two proposed systems for computer-aided diagnosis (CAD) to detect and classify three types of white blood cells which are fundamental of an acute leukemia diagnosis. Both systems depend on the You Only Look Once (YOLOv2) algorithm based on Convolutional Neural Network (CNN). The first system detects and classifies leukocytes at the same time called computer-aided diagnosis with one model (CADM1). The second system separates detection and classification by using two models called computer-aided diagnosis with two models (CADM2). The main purpose of the paper is proving the high performance and accuracy by fragmentation of the main task into sub-tasks through comparing between CADM1 and CADM2. Also, the paper proved that can be depending only on deep learning without any traditional segmentation and preprocessing on the microscopic image. The (CADM1) achieved average precision for detection and classification class1=56%, class2=69% and class3 72% while (CADM2) achieved average precision up to 94% for detect leukocytes and accuracy 92.4% for classification. The result of the second system is very suitable for diagnosis leukocytes in leukemia.

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.

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