http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
A Comparative Study of Alzheimer’s Disease Classification using Multiple Transfer Learning Models
Deekshitha Prakash,Nuwan Madusanka,Subrata Bhattacharjee,Hyeon-Gyun Park,Cho-Hee Kim,최흥국 한국멀티미디어학회 2019 The journal of multimedia information system Vol.6 No.4
Over the past decade, researchers were able to solve complex medical problems as well as acquire deeper understanding of entire issue due to the availability of machine learning techniques, particularly predictive algorithms and automatic recognition of patterns in medical imaging. In this study, a technique called transfer learning has been utilized to classify Magnetic Resonance (MR) images by a pre-trained Convolutional Neural Network (CNN). Rather than training an entire model from scratch, transfer learning approach uses the CNN model by fine-tuning them, to classify MR images into Alzheimer’s disease (AD), mild cognitive impairment (MCI) and normal control (NC). The performance of this method has been evaluated over Alzheimer’s Disease Neuroimaging (ADNI) dataset by changing the learning rate of the model. Moreover, in this study, in order to demonstrate the transfer learning approach we utilize different pre-trained deep learning models such as GoogLeNet, VGG-16, AlexNet and ResNet-18, and compare their efficiency to classify AD. The overall classification accuracy resulted by GoogLeNet for training and testing was 99.84% and 98.25% respectively, which was exceptionally more than other models training and testing accuracies.
Bhattacharjee, Subrata,Prakash, Deekshitha,Kim, Cho-Hee,Choi, Heung-Kook Korea Multimedia Society 2020 멀티미디어학회논문지 Vol.23 No.12
The analysis of digital microscopy images plays a vital role in computer-aided diagnosis (CAD) and prognosis. The main purpose of this paper is to develop a machine learning technique to predict the histological grades in prostate biopsy. To perform a multiclass classification, an AI-based deep learning algorithm, a multichannel convolutional neural network (MCCNN) was developed by connecting layers with artificial neurons inspired by the human brain system. The histological grades that were used for the analysis are benign, grade 3, grade 4, and grade 5. The proposed approach aims to classify multiple patterns of images extracted from the whole slide image (WSI) of a prostate biopsy based on the Gleason grading system. The Multichannel Convolution Neural Network (MCCNN) model takes three input channels (Red, Green, and Blue) to extract the computational features from each channel and concatenate them for multiclass classification. Stain normalization was carried out for each histological grade to standardize the intensity and contrast level in the image. The proposed model has been trained, validated, and tested with the histopathological images and has achieved an average accuracy of 96.4%, 94.6%, and 95.1%, respectively.
Subrata Bhattacharjee,Deekshitha Prakash,김초희,김희철,최흥국 대한의료정보학회 2022 Healthcare Informatics Research Vol.28 No.1
Objectives: A primary brain tumor starts to grow from brain cells, and it occurs as a result of errors in the DNA of normalcells. Therefore, this study was carried out to analyze the two-dimensional (2D) texture, morphology, and statistical featuresof brain tumors and to perform a classification using artificial intelligence (AI) techniques. Methods: AI techniques can helpradiologists to diagnose primary brain tumors without using any invasive measurement techniques. In this paper, we focusedon deep learning (DL) and machine learning (ML) techniques for texture, morphological, and statistical feature classificationof three tumor types (namely, glioma, meningioma, and pituitary). T1-weighted magnetic resonance imaging (MRI) 2D scanswere used for analysis and classification (multiclass and binary). A total of 102 features were calculated for each tumor, and the20 most significant features were selected using the three-step feature selection method, which included removing duplicatefeatures, Pearson correlations, and recursive feature elimination. Results: From the predicted results of multiclass and binaryclassification, a long short-term memory binary classification (glioma vs. meningioma) showed the best performance, withan average accuracy, recall, precision, F1-score, and kappa coefficient of 97.7%, 97.2%, 97.5%, 97.0%, and 94.7%, respectively. Conclusions: The early diagnosis of primary brain tumors is very important because it can be the key to effective treatment. Therefore, this research presents a method for early diagnoses by effectively classifying three types of primary brain tumors.