An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

Billah, Mustain; Waheed, Sajjad; Rahman, Mohammad Motiur

doi:10.1155/2017/9545920

Cited by 92 publications

(47 citation statements)

References 21 publications

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“…In addition, AI has powerful algorithms that can be used to enhance medical tasks and skills, thus overcoming fatigue, distraction, out of date in new diagnosis techniques or age-related impairment of the visual sense in physicians [6]. The most relevant applications of AI involve machine learning (ML) techniques which include enhancing cancer diagnosis [7]: the human physician error rate was reduced by 85% in metastatic breast cancer detection [8]; improve early detection of polyps to prevent colorectal cancer [9,10], with accuracy achieved greater than 98%; and classification of tissues and subsequent recognition of cardiovascular organs [11]. Other promising applications include the rapid identification of radiographic anomalies [12], delineation of surgical anatomy [13] and classification of malignant tissues in pathologic specimens [14], assisted by computer vision techniques.…”

Section: Introductionmentioning

confidence: 99%

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

et al. 2020

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In medicine, a misdiagnosis or the absence of specialists can affect the patient's health, leading to unnecessary tests and increasing the costs of healthcare. In particular, the lack of specialists in otolaryngology in third world countries forces patients to seek medical attention from general practitioners, whom might not have enough training and experience for making correct diagnosis in this field. To tackle this problem, we propose and test a computer-aided system based on machine learning models and image processing techniques for otoscopic examination, as a support for a more accurate diagnosis of ear conditions at primary care before specialist referral; in particular, for myringosclerosis, earwax plug, and chronic otitis media. To characterize the tympanic membrane and ear canal for each condition, we implemented three different feature extraction methods: color coherence vector, discrete cosine transform, and filter bank. We also considered three machine learning algorithms: support vector machine (SVM), k-nearest neighbor (k-NN) and decision trees to develop the ear condition predictor model. To conduct the research, our database included 160 images as testing set and 720 images as training and validation sets of 180 patients. We repeatedly trained the learning models using the training dataset and evaluated them using the validation dataset to thus obtain the best feature extraction method and learning model that produce the highest validation accuracy. The results showed that the SVM and k-NN presented the best performance followed by decision trees model. Finally, we performed a classification stage-i.e., diagnosis-using testing data, where the SVM model achieved an average classification accuracy of 93.9%, average sensitivity of 87.8%, average specificity of 95.9%, and average positive predictive value of 87.7%. The results show that this system might be used for general practitioners as a reference to make better decisions in the ear pathologies diagnosis.

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Section: Introductionmentioning

confidence: 99%

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

et al. 2020

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“…1 bottom row). One explanation for the currently achieved values is the small training data set, as it is known from literature [11,12,13] that more and diverse training data in the range of 100,000 [11] images and more will provide better results. Thus, we are currently extending our training data set in order to achieve better results.…”

Section: Resultsmentioning

confidence: 99%

“…Mo et al [11] also apply this approach of Faster R-CNNs [7] for polyp detection. Other groups [12,13] suggest other DCNN architectures, including a combination of Wavelets and deep learning [12], or SegNet [13]. In contrast to the fore mentioned classical approaches, the deep learning methods rely on much more training data with up to 100,000 images from 2,000 patients, yielding results up to 98% [11].…”

Section: State Of the Artmentioning

confidence: 99%

Computer Aided Detection of Polyps in Whitelight- Colonoscopy Images using Deep Neural Networks

Wittenberg

Zobel

Rathke

et al. 2019

Current Directions in Biomedical Engineering

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Early detection of polyps is one central goal of colonoscopic screening programs. To support gastroenterologists during this examination process, deep convolutional neural network can be applied for computer-assisted detection of neoplastic lesions. In this work, a Mask R-CNN architecture was applied. For training and testing, three independent colonoscopy data sets were used, including 2484 HD labelled images with polyps from our clinic, as well as two public image data sets from the MICCAI 2015 polyp detection challenge, consisting of 612 SD and 194 HD labelled images with polyps. After training the deep neural network, best results for the three test data sets were achieved in the range of recall = 0.92, precision = 0.86, F1 = 0.89 (data set A), rec = 0.86, prec = 0.80, F1 = 0.82 (data set B) and rec = 0.83, prec = 0.74, F1 = 0.79 (data set C).

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“…In this step, pointwise‐cross‐feature map is computed for localization. Later, color wavelet features are merged with CNN features to perform classification through SVM (Billah, Waheed, & Rahman, ).…”

Section: Related Workmentioning

confidence: 99%

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection

Majid

Khan

Yasmin

et al. 2020

Microscopy Res & Technique

125

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Automated detection and classification of gastric infections (i.e., ulcer, polyp, esophagitis, and bleeding) through wireless capsule endoscopy (WCE) is still a key challenge. Doctors can identify these endoscopic diseases by using the computer-aided diagnostic (CAD) systems. In this article, a new fully automated system is proposed for the recognition of gastric infections through multi-type features extraction, fusion, and robust features selection. Five key steps are performed-database creation, handcrafted and convolutional neural network (CNN) deep features extraction, a fusion of extracted features, selection of best features using a genetic algorithm (GA), and recognition. In the features extraction step, discrete cosine transform, discrete wavelet transform strong color feature, and VGG16-based CNN features are extracted. Later, these features are fused by simple array concatenation and GA is performed through which best features are selected based on K-Nearest Neighbor fitness function. In the last, best selected features are provided to Ensemble classifier for recognition of gastric diseases. A database is prepared using four datasets-Kvasir, CVC-ClinicDB, Private, and ETIS-LaribPolypDB with four types of gastric infections such as ulcer, polyp, esophagitis, and bleeding. Using this database, proposed technique performs better as compared to existing methods and achieves an accuracy of 96.5%. K E Y W O R D S

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An Automatic Gastrointestinal Polyp Detection System in Video Endoscopy Using Fusion of Color Wavelet and Convolutional Neural Network Features

Cited by 92 publications

References 21 publications

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

Computer-aided diagnosis of external and middle ear conditions: A machine learning approach

Computer Aided Detection of Polyps in Whitelight- Colonoscopy Images using Deep Neural Networks

Classification of stomach infections: A paradigm of convolutional neural network along with classical features fusion and selection

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