An automated deep learning based pancreatic tumor diagnosis and classification model using computed tomography images

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ajanthaa Lakkshmanan ◽  
C. Anbu Ananth ◽  
S. Tiroumalmouroughane S. Tiroumalmouroughane
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Pancreatic Tumor ◽  
Classification Model ◽  
Tumor Segmentation ◽  
Content Type ◽  
Computed Tomography Images ◽  
Significant Performance ◽  
Comparative Results ◽  
Bpnn Model

PurposeThe advancements of deep learning (DL) models demonstrate significant performance on accurate pancreatic tumor segmentation and classification.Design/methodology/approachThe presented model involves different stages of operations, namely preprocessing, image segmentation, feature extraction and image classification. Primarily, bilateral filtering (BF) technique is applied for image preprocessing to eradicate the noise present in the CT pancreatic image. Besides, noninteractive GrabCut (NIGC) algorithm is applied for the image segmentation process. Subsequently, residual network 152 (ResNet152) model is utilized as a feature extractor to originate a valuable set of feature vectors. At last, the red deer optimization algorithm (RDA) tuned backpropagation neural network (BPNN), called RDA-BPNN model, is employed as a classification model to determine the existence of pancreatic tumor.FindingsThe experimental results are validated in terms of different performance measures and a detailed comparative results analysis ensured the betterment of the RDA-BPNN model with the sensitivity of 98.54%, specificity of 98.46%, accuracy of 98.51% and F-score of 98.23%.Originality/valueThe study also identifies several novel automated deep learning based approaches used by researchers to assess the performance of the RDA-BPNN model on benchmark dataset and analyze the results in terms of several measures.

scholarly journals Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans

2022 ◽  
Vol 2022 ◽  
pp. 1-15
Author(s):  
Maha M. Althobaiti ◽  
Ahmed Almulihi ◽  
Amal Adnan Ashour ◽  
Romany F. Mansour ◽  
Deepak Gupta
Keyword(s):  
Deep Learning ◽  
Pancreatic Tumor ◽  
Image Interpretation ◽  
Classification Model ◽  
Pancreatic Tumors ◽  
Computed Tomography Scans ◽  
Current Scenario ◽  
Comparative Results ◽  
Study Designs ◽  
Aided Diagnosis

Pancreatic tumor is a lethal kind of tumor and its prediction is really poor in the current scenario. Automated pancreatic tumor classification using computer-aided diagnosis (CAD) model is necessary to track, predict, and classify the existence of pancreatic tumors. Artificial intelligence (AI) can offer extensive diagnostic expertise and accurate interventional image interpretation. With this motivation, this study designs an optimal deep learning based pancreatic tumor and nontumor classification (ODL-PTNTC) model using CT images. The goal of the ODL-PTNTC technique is to detect and classify the existence of pancreatic tumors and nontumor. The proposed ODL-PTNTC technique includes adaptive window filtering (AWF) technique to remove noise existing in it. In addition, sailfish optimizer based Kapur’s Thresholding (SFO-KT) technique is employed for image segmentation process. Moreover, feature extraction using Capsule Network (CapsNet) is derived to generate a set of feature vectors. Furthermore, Political Optimizer (PO) with Cascade Forward Neural Network (CFNN) is employed for classification purposes. In order to validate the enhanced performance of the ODL-PTNTC technique, a series of simulations take place and the results are investigated under several aspects. A comprehensive comparative results analysis stated the promising performance of the ODL-PTNTC technique over the recent approaches.

The Brain Tumor Segmentation Using Fuzzy C-Means Technique

2018 ◽  
pp. 2402-2419
Author(s):  
Jyotsna Rani ◽  
Ram Kumar ◽  
Fazal A. Talukdar ◽  
Nilanjan Dey
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Area Of Interest ◽  
Fuzzy C Means Clustering ◽  
Comparative Results ◽  
The Brain

Image segmentation is a technique which divides an image into its constituent regions or objects. Segmentation continues till we reach our area of interest or the specified object of target. This field offers vast future scope and challenges for the researchers. This proposal uses the fuzzy c mean technique to segment the different MRI brain tumor images. This proposal also shows the comparative results of Thresholding, K-means clustering and Fuzzy c- means clustering. Dice coefficient and Jaccards measure is used for accuracy of the segmentation in this proposal. Experimental results demonstrate the performance of the designed method.

scholarly journals Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies

2020 ◽  
Vol 6 (11) ◽  
pp. 125 ◽  
Author(s):  
Albert Comelli ◽  
Claudia Coronnello ◽  
Navdeep Dahiya ◽  
Viviana Benfante ◽  
Stefano Palmucci ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
High Resolution ◽  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Dice Similarity Coefficient ◽  
Learning Models ◽  
High Resolution Computed Tomography ◽  
Computed Tomography Images ◽  
Self Driving Cars

Background: The aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim to enhance the methodology performed by healthcare operators in radiomics studies where operator-independent segmentation methods must be used to correctly identify the target and, consequently, the texture-based prediction model. Methods: Two deep learning models were investigated: (i) U-Net, already used in many biomedical image segmentation tasks, and (ii) E-Net, used for image segmentation tasks in self-driving cars, where hardware availability is limited and accurate segmentation is critical for user safety. Our small image dataset is composed of 42 studies of patients with idiopathic pulmonary fibrosis, of which only 32 were used for the training phase. We compared the performance of the two models in terms of the similarity of their segmentation outcome with the gold standard and in terms of their resources’ requirements. Results: E-Net can be used to obtain accurate (dice similarity coefficient = 95.90%), fast (20.32 s), and clinically acceptable segmentation of the lung region. Conclusions: We demonstrated that deep learning models can be efficiently applied to rapidly segment and quantify the parenchyma of patients with pulmonary fibrosis, without any radiologist supervision, in order to produce user-independent results.

scholarly journals Deep Learning Based Liver Cancer Segmentation from Computed Tomography Images

2020 ◽  
Author(s):  
Yodit Abebe Ayalew ◽  
Kinde Anlay Fante ◽  
Mohammed Aliy
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Liver Cancer ◽  
Ct Scan ◽  
Tumor Segmentation ◽  
Cancer Death ◽  
Liver Segmentation ◽  
Abdominal Ct ◽  
Computed Tomography Images ◽  
Abdominal Ct Scan

Abstract Background: Liver cancer is the sixth most common cancer worldwide. According to WHO data in 2017, the liver cancer death in Ethiopia reached 1040 (0.16%) from all cancer deaths. Hepatocellular carcinoma (HCC), primary liver cancer causes the death of around 700,000 people each year worldwide and this makes it the third leading cause of cancer death. HCC is occurred due to cirrhosis and hepatitis B or C viruses. Liver cancer mostly diagnosed with a computed tomography (CT) scan. But, the detection of the tumor from the CT scan image is difficult since tumors have similar intensity with nearby tissues and may have a different appearance depending on their type, state, and equipment setting. Nowadays deep learning methods have been used for the segmentation of liver and its tumor from the CT scan images and they are more efficient than those traditional methods. But, they are computationally expensive and need many labeled samples for training, which are difficult in the case of biomedical images. Results: A deep learning-based segmentation algorithm is employed for liver and tumor segmentation from abdominal CT scan images. Three separate UNet models, one for liver segmentation and the others two for tumor segmentation from the segmented liver and directly from the abdominal CT scan image were used. A dice score of 0.96 was obtained for liver segmentation. And a dice score of 0.74 and 0.63 was obtained for segmentation of tumor from the liver and from abdominal CT scan image respectively. Conclusion: The research improves the liver tumor segmentation that will help the physicians in the diagnosis and detection of liver tumors and in designing a treatment plan for the patient. And for the patient, it increases the patients’ chance of getting treatment and decrease the mortality rate due to liver cancer.

Classification of electrocardiogram signal using an ensemble of deep learning models

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Saroj Kumar Pandey ◽  
Rekh Ram Janghel
Keyword(s):  
Deep Learning ◽  
Majority Voting ◽  
Classification Model ◽  
World Health ◽  
Product Rule ◽  
Learning Models ◽  
Data Set ◽  
Raw Data ◽  
Content Type ◽  
Heartbeat Classification

PurposeAccording to the World Health Organization, arrhythmia is one of the primary causes of deaths across the globe. In order to reduce mortality rate, cardiovascular disease should be properly identified and the proper treatment for the same should be immediately provided to the patients. The objective of this paper was to implement a better heartbeat classification model which will work better than the other implemented heartbeat classification methods.Design/methodology/approachIn this paper, the ensemble of two deep learning models is proposed to classify the MIT-BIH arrhythmia database into four different classes according to ANSI-AAMI standards. First, a convolutional neural network (CNN) model is used to classify heartbeats on a raw data set. Secondly, four features (wavelets, R-R intervals, morphological and higher-order statistics) are extracted from the data set and then applied to a long short-term memory (LSTM) model to classify the heartbeats. Finally, the ensemble of CNN and LSTM model with sum rule, product rule and majority voting has been used to identify the heartbeat classes.FindingsAmong these, the highest accuracy obtained is 98.58% using ensemble method with product rule. The results show that the ensemble of CNN and BLSTM has offered satisfactory performance compared to other techniques discussed in this study.Originality/valueIn this study, we have developed a new combination of two deep learning models to enhance the performance of arrhythmia classification using segmentation of input ECG signals. The contributions of this study are as follows: First, a deep CNN model is built to classify ECG heartbeat using a raw data set. Second, four types of features (R-R interval, HOS, morphological and wavelet) were extracted from the raw data set and then applied to the bidirectional LSTM model to classify the ECG heartbeat. Third, combination rules (sum rules, product rules and majority voting rules) were tested to ensure the accumulated probabilities of the CNN and LSTM models.

scholarly journals MIScnn: a framework for medical image segmentation with convolutional neural networks and deep learning

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Dominik Müller ◽  
Frank Kramer
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Medical Image ◽  
Cross Validation ◽  
Data Augmentation ◽  
Semantic Segmentation ◽  
Medical Image Segmentation ◽  
Tumor Segmentation ◽  
Data Set ◽  
Public Data

Abstract Background The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation. Still, current image segmentation platforms do not provide the required functionalities for plain setup of medical image segmentation pipelines. Already implemented pipelines are commonly standalone software, optimized on a specific public data set. Therefore, this paper introduces the open-source Python library MIScnn. Implementation The aim of MIScnn is to provide an intuitive API allowing fast building of medical image segmentation pipelines including data I/O, preprocessing, data augmentation, patch-wise analysis, metrics, a library with state-of-the-art deep learning models and model utilization like training, prediction, as well as fully automatic evaluation (e.g. cross-validation). Similarly, high configurability and multiple open interfaces allow full pipeline customization. Results Running a cross-validation with MIScnn on the Kidney Tumor Segmentation Challenge 2019 data set (multi-class semantic segmentation with 300 CT scans) resulted into a powerful predictor based on the standard 3D U-Net model. Conclusions With this experiment, we could show that the MIScnn framework enables researchers to rapidly set up a complete medical image segmentation pipeline by using just a few lines of code. The source code for MIScnn is available in the Git repository: https://github.com/frankkramer-lab/MIScnn.

The Brain Tumor Segmentation Using Fuzzy C-Means Technique

2017 ◽  
pp. 40-61 ◽  
Author(s):  
Jyotsna Rani ◽  
Ram Kumar ◽  
Fazal A. Talukdar ◽  
Nilanjan Dey
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Fuzzy C Means ◽  
Mri Brain ◽  
Area Of Interest ◽  
Fuzzy C Means Clustering ◽  
Comparative Results ◽  
The Brain

Image segmentation is a technique which divides an image into its constituent regions or objects. Segmentation continues till we reach our area of interest or the specified object of target. This field offers vast future scope and challenges for the researchers. This proposal uses the fuzzy c mean technique to segment the different MRI brain tumor images. This proposal also shows the comparative results of Thresholding, K-means clustering and Fuzzy c- means clustering. Dice coefficient and Jaccards measure is used for accuracy of the segmentation in this proposal. Experimental results demonstrate the performance of the designed method.

scholarly journals Modified U-Net for liver cancer segmentation from computed tomography images with a new class balancing method

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Yodit Abebe Ayalew ◽  
Kinde Anlay Fante ◽  
Mohammed Aliy Mohammed
Keyword(s):  
Computed Tomography ◽  
Deep Learning ◽  
Liver Cancer ◽  
Ct Scan ◽  
Tumor Segmentation ◽  
Class Imbalance Problem ◽  
Abdominal Ct ◽  
New Class ◽  
Computed Tomography Images ◽  
Abdominal Ct Scan

Abstract Background Liver cancer is the sixth most common cancer worldwide. It is mostly diagnosed with a computed tomography scan. Nowadays deep learning methods have been used for the segmentation of the liver and its tumor from the computed tomography (CT) scan images. This research mainly focused on segmenting liver and tumor from the abdominal CT scan images using a deep learning method and minimizing the effort and time used for a liver cancer diagnosis. The algorithm is based on the original UNet architecture. But, here in this paper, the numbers of filters on each convolutional block were reduced and new batch normalization and a dropout layer were added after each convolutional block of the contracting path. Results Using this algorithm a dice score of 0.96, 0.74, and 0.63 were obtained for liver segmentation, segmentation of tumors from the liver, and the segmentation of tumor from abdominal CT scan images respectively. The segmentation results of liver and tumor from the liver showed an improvement of 0.01 and 0.11 respectively from other works. Conclusion This work proposed a liver and a tumor segmentation method using a UNet architecture as a baseline. Modification regarding the number of filters and network layers were done on the original UNet model to reduce the network complexity and improve segmentation performance. A new class balancing method is also introduced to minimize the class imbalance problem. Through these, the algorithm attained better segmentation results and showed good improvement. However, it faced difficulty in segmenting small and irregular tumors.

scholarly journals Application of Machine Learning and Deep Learning Methods for Brain Tumor Identification and Classification

2020 ◽  
Vol 8 (6) ◽  
pp. 2886-2891
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Deep Learning ◽  
Brain Tumor ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Survival Ratio ◽  
Reliable Technique ◽  
Tumor Identification ◽  
Mri Image

In the area of medical imaging technology, advances in Artificial intelligence (AI) delivers promising solutions with higher accuracy. For healthcare solutions, medical images provides a systematic way for diagnosis the diseases earlier and make treatments more effective. Machine learning and deep learning are rapidly grown fields of AI that may apply to many domains including image processing, speech recognition and text understanding. As MRI image segmentation is a key task for identification of brain anomalies, a fast and reliable technique is essential for increasing the survival ratio of affected patients. Manual segmentation of the brain MRI image involves more time and it may subject to inaccuracies. Hence, AI approaches and algorithms have been developed for tumor segmentation. This paper contains the detailed study of the available methods of machine learning and deep learning for brain tumor identification and classification through MRI image segmentation. It discusses and summarizes the methodologies and its results available for classification of brain tumor.

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