Goutam Chandra Saha

Bio: Goutam Chandra Saha is an academic researcher. The author has contributed to research in topics: Computer science & Artificial intelligence. The author has co-authored 1 publications.

Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age

Abstract: Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting that, in most circumstances, the proposed technique outperforms both existing SVM and AlexNet. The overall accuracy obtained is 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%), with best accuracy of 100%, 92%, 92%, and 81%, respectively.

Localized multiple kernel learning using graph modularity

Abstract: • A graph-based heuristic approach for multiple kernel learning (MKL). • Assigns sample-specific kernel weights based on contribution to graph modularity . • Classification performance comparable to state-of-the-art MKL algorithms. • Outperforms other heuristic approaches and has low time complexity. Multiple kernel learning (MKL) algorithms exploit information from multiple feature representations by assigning weights to each representation in the kernel space, and later combining them. However, this ignores the fact that data points exhibit locally varying characteristics. To address this problem, localized MKL algorithms learn locality-specific kernel weights which determine each base kernel’s influence in the locality under consideration. Here, we relate the problem of determining the relevance of base kernels for classification to that of quantifying community structure in graphs. Next, we derive sample-specific kernel weights using graph modularity. Through experiments on publicly available datasets, we show that the proposed approach offers a viable alternative to state-of-the-art MKL approaches while being computationally inexpensive.

Nx-IoT: Improvement of Conventional IoT Framework by Incorporating SDN Infrastructure

Abstract: The rapid advancement of the Internet of Things (IoT) in real-world applications has attracted immense research endeavors in the last few years. It has got tremendous potential in industrial automation and various other fields. The use of IoT has changed the perspective of general applications in today’s world. In this article, a next-generation architecture, Nx-IoT, is proposed for conventional 6LoWPAN-based IoT. The proposed Nx-IoT architecture works in two modes: 1) single controller-based (6SSDx) and 2) multicontroller-based (6MSDx). The Nx-IoT architecture uses new algorithms for routing management and load distribution among the SDN controllers. The experimentation is carried out in a prototype testbed environment. The result shows improved performances in terms of round trip and packet drop, compared to the conventional 6LoWPAN and cloud system. The proposed Nx-IoT architecture reduces the latency and shows better throughput performances as compared to the existing state of the art.

Automated path selection technique while incorporating multiple assay operations and cross-contamination avoidance in cross-referencing DMFBs

Abstract: Digital Microfluidic Biochips (DMFBs) perform many biochemical reactions requiring relatively less cost and very less amount of space. DMFBs that use cross-referencing addressing requires less number of pins, therefore, less manufacturing cost. However, it suffers from a problem called electrode interference, i.e., unwanted droplet operation because of an extra activated cell. DMFBs also suffer from a problem called cross-contamination, i.e., mixing of droplets with unwanted residues of droplets containing different chemicals which results in incorrect diagnosis. In this article, our objective is whether a cross-referencing DMFB with an efficient module placement design can be declared as a multifunctional chip or not. We propose a chip design which incorporates parallelism for enhancing performance in terms of assay completion time while performing multiple types of bioassays. We also propose a novel method, which automatically selects a new cross-contamination free path while routing from the source to the sink. We have included an on-chip washing scheme. The whole method ensures no Electrode Interference.

Classification of Valvular Regurgitation Using Echocardiography

Abstract: Echocardiography (echo) is a commonly utilized tool in the diagnosis of various forms of valvular heart disease for its ability to detect types of cardiac regurgitation. Regurgitation represents irregularities in cardiac function and the early detection of regurgitation is necessary to avoid invasive cardiovascular surgery. In this paper, we focussed on the classification of regurgitations from videographic echo images. Three different types of regurgitation are considered in this work, namely, aortic regurgitation (AR), mitral regurgitation (MR), and tricuspid regurgitation (TR). From the echo images, texture features are extracted, and classification is performed using Random Forest (RF) classifier. Extraction of keyframe is performed from the video file using two approaches: a reference frame keyframe extraction technique and a redundant frame removal technique. To check the robustness of the model, we have considered both segmented and nonsegmented frames. Segmentation is carried out after keyframe extraction using the Level Set (LS) with Fuzzy C-means (FCM) approach. Performances are evaluated in terms of accuracy, precision, recall, and F1-score and compared for both reference frame and redundant frame extraction techniques. K-fold cross-validation is used to examine the performance of the model. The performance result shows that our proposed approach outperforms other state-of-art machine learning approaches in terms of accuracy, precision, recall, and F1-score.

HRDEL: High ranking deep ensemble learning-based lung cancer diagnosis model

Abstract: Among all the diseases in human beings, lung cancer is known as the most hazardous disease that often leads to death rather than other cancer ailments. Lung cancer is asymptomatic, and so, it is unable to detect at the early stage. But, the rapid identification of lung cancer helps for sustaining the survival rate of people. Hence, many researchers develop various techniques for detecting lung cancer by undergoing different studies. Recently, computer technology has been used for solving these diagnosis problems. These developed systems involve diverse deep and machine learning approaches along with certain image-processing techniques for forecasting the severity level of lung cancer. Hence, this methodology plans to develop a novel intelligent method for diagnosing lung cancer. Initially, data is gathered by downloading two benchmark datasets, which include attribute information from various patients' health records. Furthermore, two standard techniques, “Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE)” have been used to extract features. Further, the deep features are retrieved from “the pooling layer of Convolutional Neural Network (CNN)”. Further to choose the significant features, the feature selection is taken place by the Best Fitness-based Squirrel Search Algorithm (BF-SSA), which is known as optimal feature selection. This hybrid optimization concept is considered to be superior in various domains to explore the search space efficiently and makes better performance in exploiting the feature selection. In the final phase called prediction, High Ranking Deep Ensemble Learning (HR-DEL) takes place concerning five forms of detection models. Finally, the high ranking of all the classifiers yields the final predicted output. The developed HR-DEL makes accurate prediction up to 8.79% better than the conventional methods and provides high robustness by reducing the dispersion or spread of the classification and model efficiency. The classification is performed, and the results are evaluated with the performance comparison of various algorithms.

Classifying Brain Tumors on Magnetic Resonance Imaging by Using Convolutional Neural Networks

Abstract: The study of neuroimaging is a very important tool in the diagnosis of central nervous system tumors. This paper presents the evaluation of seven deep convolutional neural network (CNN) models for the task of brain tumor classification. A generic CNN model is implemented and six pre-trained CNN models are studied. For this proposal, the dataset utilized in this paper is Msoud, which includes Fighshare, SARTAJ, and Br35H datasets, containing 7023 MRI images. The magnetic resonance imaging (MRI) in the dataset belongs to four classes, three brain tumors, including Glioma, Meningioma, and Pituitary, and one class of healthy brains. The models are trained with input MRI images with several preprocessing strategies applied in this paper. The CNN models evaluated are Generic CNN, ResNet50, InceptionV3, InceptionResNetV2, Xception, MobileNetV2, and EfficientNetB0. In the comparison of all CNN models, including a generic CNN and six pre-trained models, the best CNN model for this dataset was InceptionV3, which obtained an average Accuracy of 97.12%. The development of these techniques could help clinicians specializing in the early detection of brain tumors.

An Enhanced Machine Learning Approach for Brain MRI Classification

Abstract: Magnetic Resonance Imaging (MRI) is a noninvasive technique used in medical imaging to diagnose a variety of disorders. The majority of previous systems performed well on MRI datasets with a small number of images, but their performance deteriorated when applied to large MRI datasets. Therefore, the objective is to develop a quick and trustworthy classification system that can sustain the best performance over a comprehensive MRI dataset. This paper presents a robust approach that has the ability to analyze and classify different types of brain diseases using MRI images. In this paper, global histogram equalization is utilized to remove unwanted details from the MRI images. After the picture has been enhanced, a symlet wavelet transform-based technique has been suggested that can extract the best features from the MRI images for feature extraction. On gray scale images, the suggested feature extraction approach is a compactly supported wavelet with the lowest asymmetry and the most vanishing moments for a given support width. Because the symlet wavelet can accommodate the orthogonal, biorthogonal, and reverse biorthogonal features of gray scale images, it delivers higher classification results. Following the extraction of the best feature, the linear discriminant analysis (LDA) is employed to minimize the feature space’s dimensions. The model was trained and evaluated using logistic regression, and it correctly classified several types of brain illnesses based on MRI pictures. To illustrate the importance of the proposed strategy, a standard dataset from Harvard Medical School and the Open Access Series of Imaging Studies (OASIS), which encompasses 24 different brain disorders (including normal), is used. The proposed technique achieved the best classification accuracy of 96.6% when measured against current cutting-edge systems.

Review of Computer Aided-Diagnosis (CAD) Systems for MRI Gliomas brain tumors explorations based on Machine Learning and Deep learning

Abstract: During clinical routines, the manual Gliomas brain tumor exploration, via MRI anatomical modalities, is considered as critical task and time-consuming. Therefore, the Computer-aided diagnosis (CAD) system is highly recommended by Neuro-radiologists to ensure an accurate diagnosis and reduce the time required for diagnosis.Due to the revolution of Artificial Intelligence (AI) technology in the field of medical imaging analysis, several brain MRI CAD tools based on Deep Learning (DL) and Machine Learning (ML) have been presented in the literature. This work proposed a comprehensive review of recent trends of CAD tools for Gliomas brain tumors exploration with regards to DL and ML.In our study, we focused on three key steps involved generally in the implementation of CAD system mainly: the pre-processing, the segmentation, and the tumor grade classification. An objective evaluation via performance evaluation of state-of-the-art DL based-approaches s for MR images analysis is also addressed in this paper. We could confirm, based on the compared methods results, that a combination of a set of DL techniques will provide more accurate segmentation results rather than relying on a single specific technique.