Detection and Classification of Gastrointestinal Diseases using Machine Learning

2020 ◽  
Vol 16 ◽  
Author(s):  
Javeria Naz ◽  
Muhammad Sharif ◽  
Mussarat Yasmin ◽  
Mudassar Raza ◽  
Muhammad Attique Khan
Keyword(s):  
State Of The Art ◽  
Gastrointestinal Diseases ◽  
Automated Classification ◽  
Automated Recognition ◽  
Gi Infections ◽  
Wireless Capsule ◽  
Aided Diagnosis ◽  
New Algorithms ◽  
Expert Physician

Background: Traditional endoscopy is an invasive and painful method of examining the gastrointestinal tract (GIT) not supported by the physicians and patients. To handle this issue, video endoscopy (VE) or wireless capsule endoscopy (WCE) is recommended and utilized for GIT examination. Furthermore, manual assessment of captured images is not possible for an expert physician because it’s a time taking task to analyze thousands of images thoroughly. Hence, there comes the need for a Computer-Aided-Diagnosis (CAD) method to help doctors in the analysis of images. Many researchers have proposed techniques for automated recognition and classification of abnormality in captured images. Introduction: In this article, existing methods for automated classification, segmentation and detection of several GI diseases are discussed. Paper gives a comprehensive detail about these state-of-the-art methods. Furthermore, literature is divided into several subsections based on preprocessing techniques, segmentation techniques, handcrafted features based techniques and deep learning based techniques. Finally, issues, challenges and limitations are also undertaken. Conclusion: This comprehensive review article combines information related to a number of GI diseases diagnosis methods at one place. Study of this article will facilitate the researchers to develop new algorithms and approaches for early detection of GI diseases detection with more promising results as compared to the existing ones of literature. Results: A comparative analysis of different approaches for the detection and classification of GI infections.

scholarly journals An Ingenious Design of a High Performance-Low Complexity Image Compressor for Wireless Capsule Endoscopy

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1617
Author(s):  
Ioannis Intzes ◽  
Hongying Meng ◽  
John Cosmas
Keyword(s):  
Power Consumption ◽  
Capsule Endoscopy ◽  
State Of The Art ◽  
Logic Gates ◽  
Low Complexity ◽  
Gastrointestinal Diseases ◽  
Wireless Capsule Endoscopy ◽  
Huffman Coding ◽  
Medical Diagnoses ◽  
Wireless Capsule

Wireless Capsule Endoscopy is a state-of-the-art technology for medical diagnoses of gastrointestinal diseases. The amount of data produced by an endoscopic capsule camera is huge. These vast amounts of data are not practical to be saved internally due to power consumption and the available size. So, this data must be transmitted wirelessly outside the human body for further processing. The data should be compressed and transmitted efficiently in the domain of power consumption. In this paper, a new approach in the design and implementation of a low complexity, multiplier-less compression algorithm is proposed. Statistical analysis of capsule endoscopy images improved the performance of traditional lossless techniques, like Huffman coding and DPCM coding. Furthermore the Huffman implementation based on simple logic gates and without the use of memory tables increases more the speed and reduce the power consumption of the proposed system. Further analysis and comparison with existing state-of-the-art methods proved that the proposed method has better performance.

Automated Classification of Focal and Non-Focal EEG Signals Based on Bivariate Empirical Mode Decomposition

2018 ◽  
pp. 13-33 ◽  
Author(s):  
Rajeev Sharma ◽  
Ram Bilas Pachori
Keyword(s):  
Empirical Mode Decomposition ◽  
Support Vector ◽  
Automated Classification ◽  
Intrinsic Mode Functions ◽  
Eeg Signals ◽  
Mode Decomposition ◽  
Electroencephalogram Eeg ◽  
Aided Diagnosis ◽  
Bivariate Empirical Mode Decomposition

The chapter presents a new approach of computer aided diagnosis of focal electroencephalogram (EEG) signals by applying bivariate empirical mode decomposition (BEMD). Firstly, the focal and non-focal EEG signals are decomposed using the BEMD, which results in intrinsic mode functions (IMFs) corresponding to each signal. Secondly, bivariate bandwidths namely, amplitude bandwidth, precession bandwidth, and deformation bandwidth are computed for each obtained IMF. Interquartile range (IQR) values of bivariate bandwidths of IMFs are employed as the features for classification. In order to perform classification least squares support vector machine (LS-SVM) is used. The results of the experiment suggest that the computed bivariate bandwidths are significantly useful to discriminate focal EEG signals. The resultant classification accuracy obtained using proposed methodology, applied on the Bern-Barcelona EEG database, is 84.01%. The obtained results are encouraging and the proposed methodology can be helpful for identification of epileptogenic focus.

scholarly journals Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms

2013 ◽  
Vol 2013 ◽  
pp. 1-22 ◽  
Author(s):  
Ammara Masood ◽  
Adel Ali Al-Jumaily
Keyword(s):  
Skin Cancer ◽  
State Of The Art ◽  
Comparative Assessment ◽  
Future Research ◽  
Feature Extraction And Selection ◽  
Diagnostic Models ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Diagnostic Support

Image-based computer aided diagnosis systems have significant potential for screening and early detection of malignant melanoma. We review the state of the art in these systems and examine current practices, problems, and prospects of image acquisition, pre-processing, segmentation, feature extraction and selection, and classification of dermoscopic images. This paper reports statistics and results from the most important implementations reported to date. We compared the performance of several classifiers specifically developed for skin lesion diagnosis and discussed the corresponding findings. Whenever available, indication of various conditions that affect the technique’s performance is reported. We suggest a framework for comparative assessment of skin cancer diagnostic models and review the results based on these models. The deficiencies in some of the existing studies are highlighted and suggestions for future research are provided.

scholarly journals A Transfer Learning Method for Pneumonia Classification and Visualization

2020 ◽  
Vol 10 (8) ◽  
pp. 2908 ◽  
Author(s):  
Juan Luján-García ◽  
Cornelio Yáñez-Márquez ◽  
Yenny Villuendas-Rey ◽  
Oscar Camacho-Nieto
Keyword(s):  
Transfer Learning ◽  
Causes Of Death ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Learning Method ◽  
X Ray ◽  
Chest X Ray ◽  
Aided Diagnosis ◽  
Positive Results

Pneumonia is an infectious disease that affects the lungs and is one of the principal causes of death in children under five years old. The Chest X-ray images technique is one of the most used for diagnosing pneumonia. Several Machine Learning algorithms have been successfully used in order to provide computer-aided diagnosis by automatic classification of medical images. For its remarkable results, the Convolutional Neural Networks (models based on Deep Learning) that are widely used in Computer Vision tasks, such as classification of injuries and brain abnormalities, among others, stand out. In this paper, we present a transfer learning method that automatically classifies between 3883 chest X-ray images characterized as depicting pneumonia and 1349 labeled as normal. The proposed method uses the Xception Network pre-trained weights on ImageNet as an initialization. Our model is competitive with respect to state-of-the-art proposals. To make comparisons with other models, we have used four well-known performance measures, obtaining the following results: precision (0.84), recall (0.99), F1-score (0.91) and area under the ROC curve (0.97). These positive results allow us to consider our proposal as an alternative that can be useful in countries with a lack of equipment and specialized radiologists.

scholarly journals MP19-20 STATE-OF-THE-ART WEAKLY SUPERVISED AUTOMATED CLASSIFICATION OF PROSTATE CANCER TISSUE MICROARRAYS VIA DEEP LEARNING

2020 ◽  
Vol 203 ◽  
pp. e306
Author(s):  
Sami-Ramzi Leyh-Bannurah* ◽  
Ulrich Wolffgang ◽  
Jonathan Schmitz ◽  
Veronique Ouellet ◽  
Feryel Azzi ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Deep Learning ◽  
State Of The Art ◽  
Tissue Microarrays ◽  
Cancer Tissue ◽  
Automated Classification ◽  
Prostate Cancer Tissue ◽  
Weakly Supervised

scholarly journals An Automated Classification of Mammals and Reptiles Animal Classes Using Deep Learning

2020 ◽  
pp. 2361-2370
Author(s):  
Elham Mohammed Thabit A. ALSAADI ◽  
Nidhal K. El Abbadi
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
State Of The Art ◽  
Target Object ◽  
Automated Classification ◽  
High Learning ◽  
Deep Convolutional Neural Networks ◽  
Learning Capacity ◽  
Typical Object

Detection and classification of animals is a major challenge that is facing the researchers. There are five classes of vertebrate animals, namely the Mammals, Amphibians, Reptiles, Birds, and Fish, and each type includes many thousands of different animals. In this paper, we propose a new model based on the training of deep convolutional neural networks (CNN) to detect and classify two classes of vertebrate animals (Mammals and Reptiles). Deep CNNs are the state of the art in image recognition and are known for their high learning capacity, accuracy, and robustness to typical object recognition challenges. The dataset of this system contains 6000 images, including 4800 images for training. The proposed algorithm was tested by using 1200 images. The accuracy of the system’s prediction for the target object was 97.5%.

scholarly journals Advances in computerized MRI‐based biomarkers in Alzheimer’s disease

2021 ◽  
Vol 7 (1) ◽  
pp. 26-43
Author(s):  
Raymond Wong ◽  
Yishan Luo ◽  
Vincent Chung-tong Mok ◽  
Lin Shi
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
State Of The Art ◽  
Lewy Bodies ◽  
Clinical Settings ◽  
Early Screening ◽  
Future Studies ◽  
Magnetic Resonance Imaging Mri ◽  
Aided Diagnosis

The use of neuroimaging examinations is crucial in Alzheimer’s disease (AD), in both research and clinical settings. Over the years, magnetic resonance imaging (MRI)–based computer‐aided diagnosis has been shown to be helpful for early screening and predicting cognitive decline. Meanwhile, an increasing number of studies have adopted machine learning for the classification of AD, with promising results. In this review article, we focus on computerized MRI‐based biomarkers of AD by reviewing representative studies that used computerized techniques to identify AD patients and predict cognitive progression. We categorized these studies based on the following applications: (1) identifying AD from normal control; (2) identifying AD from other dementia types, including vascular dementia, dementia with Lewy bodies, and frontotemporal dementia; and (3) predicting conversion from NC to mild cognitive impairment (MCI) and from MCI to AD. This systematic review could act as a state‐of‐the‐art overview of this emerging field as well as a basis for designing future studies.

Klassifizierung von Schmiedeteilen mittels KNN/AI enables automated recognition and classification of geometrically complex forged parts

2019 ◽  
Vol 109 (11-12) ◽  
pp. 822-827
Author(s):  
Y. Hedicke-Claus ◽  
J. Langner ◽  
M. Stonis ◽  
B.-A. Behrens
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Automated Classification ◽  
Wird Eine ◽  
Automated Recognition ◽  
Sequence Planning ◽  
Forged Parts ◽  
Shape Class ◽  
Artificial Neural

Im Beitrag wird eine Methode auf Basis Künstlicher Neuronaler Netze vorgestellt, die es erlaubt, schmiedetechnisch hergestellte Bauteile automatisiert zur Einordnung in Formenklassen zu klassifizieren. Dadurch ist es möglich, direkt aus der CAD-Datei des Schmiedeteils eine Formenklasse und für die Auslegung des Prozesses relevante Charakteristika zu ermitteln, die in einem übergeordneten Ziel dazu genutzt werden, eine automatisierte Stadienplanung durchzuführen.   This paper presents a method for the automated classification of forged parts for classification into the Spies order of shapes by artificial neural networks. The aim is to develop a recognition program within the framework of automated forging sequence planning, which can directly identify a shape class from the CAD file of the forged part and characteristics of the forged part relevant for the design of the process.

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