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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.
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.
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.
Md Hafizur Rahman,Subrata Bhattacharjee,Yeong Byn Hwang,Hee Cheol Kim,Heung Kook Choi 한국멀티미디어학회 2024 멀티미디어학회논문지 Vol.27 No.1
Early prostate cancer diagnosis by pathologists remains challenging. Recent advances in computer-aided detection (CAD), artificial intelligence (AI), and machine learning (ML) allow prostate cancer grading. This study explored the accuracy of prostate cancer detection by deep learning techniques, particularly convolutional neural networks (CNNs). We performed three-way binary classification based on images cropped to 256 × 256 and 512 × 512 pixels using an ensemble deep CNN model. Six pre-trained CNN models (MobileNet, VGG-16, ResNet-50, DenseNet-121, Inception-V3, and EfficientNet-B0) were integrated to classify histopathological features. The overall accuracy for the combined 256 × 256 and 512×512 pixel images was 94.9%. Additionally, in separate classifications of 256×256 and 512×512 images, we achieved overall accuracies of 90.8% and 94.3%, respectively. Consequently, our method effectively distinguishes benign from malignant samples, approaching near-perfect accuracy.
Leaf Extract of Moringa oleifera Prevents Ionizing Radiation-Induced Oxidative Stress in Mice
Mahuya Sinha,Dipesh K. Das,Surajit Bhattacharjee,Subrata Majumdar,Sanjit Dey 한국식품영양과학회 2011 Journal of medicinal food Vol.14 No.10
The present study evaluated the hepatoprotective effect of aqueous ethanolic Moringa oleifera leaf extract (MoLE) against radiation-induced oxidative stress, which is assessed in terms of inflammation and lipid peroxidation. Swiss albino mice were administered MoLE (300 mg/kg of body weight) for 15 consecutive days before exposing them to a single dose of 5 Gy of 60Co γ-irradiation. Mice were sacrificed at 4 hours after irradiation. Liver was collected for immunoblotting and biochemical tests for the detection of markers of hepatic oxidative stress. Nuclear translocation of nuclear factor kappa B (NF-κB) and lipid peroxidation were augmented, whereas the superoxide dismutase (SOD), catalase (CAT), reduced glutathione (GSH), and ferric reducing antioxidant power (FRAP) values were decreased by radiation exposure. Translocation of NF-κB from cytoplasm to nucleus and lipid peroxidation were found to be inhibited, whereas increases in SOD, CAT, GSH, and FRAP were observed in the mice treated with MoLE prior to irradiation. Therefore pretreatment with MoLE protected against γ-radiation-induced liver damage. The protection may be attributed to the free radical scavenging activity of MoLE, through which it can ameliorate radiation-induced oxidative stress.