Malin Bruntha

Bio: Malin Bruntha is an academic researcher from Karunya University. The author has contributed to research in topics: Feature (computer vision) & False positive rate. The author has co-authored 1 publications.

Classification of Lung Nodule Using Hybridized Deep Feature Technique

Abstract: Deep learning techniques have become very popular among Artificial Intelligence (AI) techniques in many areas of life. Among many types of deep learning techniques, Convolutional Neural Networks (CNN) can be useful in image classification applications. In this work, a hybridized approach has been followed to classify lung nodule as benign or malignant. This will help in early detection of lung cancer and help in the life expectancy of lung cancer patients thereby reducing the mortality rate by this deadly disease scourging the world. The hybridization has been carried out between handcrafted features and deep features. The machine learning algorithms such as SVM and Logistic Regression have been used to classify the nodules based on the features. The dimensionality reduction technique, Principle Component Analysis (PCA) has been introduced to improve the performance of hybridized features with SVM. The experiments have been carried out with 14 different methods. It has been found that GLCM + VGG19 + PCA + SVM outperformed all other models with an accuracy of 94.93%, sensitivity of 90.9%, specificity of 97.36% and precision of 95.44%. The F1 score was found to be 0.93 and the AUC was 0.9843. The False Positive Rate was found to be 2.637% and False Negative Rate was 9.09%.

A Novel Hybridized Feature Extraction Approach for Lung Nodule Classification Based on Transfer Learning Technique

Abstract: Purpose: In the field of medical diagnosis, deep learning-based computer-aided detection of diseases will reduce the burden of physicians in the diagnosis of diseases especially in the case of lung cancer nodule classification. Materials and Methods: A hybridized model which integrates deep features from Residual Neural Network using transfer learning and handcrafted features from the histogram of oriented gradients feature descriptor is proposed to classify the lung nodules as benign or malignant. The intrinsic convolutional neural network (CNN) features have been incorporated and they can resolve the drawbacks of handcrafted features that do not completely reflect the specific characteristics of a nodule. In the meantime, they also reduce the need for a large-scale annotated dataset for CNNs. For classifying malignant nodules and benign nodules, radial basis function support vector machine is used. The proposed hybridized model is evaluated on the LIDC-IDRI dataset. Results: It has achieved an accuracy of 97.53%, sensitivity of 98.62%, specificity of 96.88%, precision of 95.04%, F1 score of 0.9679, false-positive rate of 3.117%, and false-negative rate of 1.38% and has been compared with other state of the art techniques. Conclusions: The performance of the proposed hybridized feature-based classification technique is better than the deep features-based classification technique in lung nodule classification.

Investigation of Deep Features in Lung Nodule Classification

Abstract: Cancer is one of the deadliest diseases that affect people worldwide, regardless of their socioeconomic situation. Head and neck cancer, brain cancer, stomach cancer, breast cancer, and pancreatic cancer are some of the different types of cancer. Lung cancer has the highest occurrence and fatality rate among all cancer diseases worldwide. It has become more prevalent in developing countries due to increased air pollution. As a result of this terrible sight, lung cancer screening and early diagnosis are now more important than ever. Taking Computed Tomography (CT) images of the entire chest region and analyzing them for any abnormalities is the most common method for detecting lung cancer at its initial stage. In the clinical diagnosis of many diseases, transfer learning and deep learning are becoming increasingly important. Lung nodules, categorized as malignant or benign, are radiographic indications of lung cancer. The application of transfer learning and deep learning in the Computed Aided Diagnosis (CAD) system to aid doctors in classifying lung nodules is proposed in this paper. Convolutional Neural Networks (CNNs) such as VGG16, VGG19, and ResNet50 are used as feature extractors in this study. In the classification of lung nodules, the performance of these deep features is explored.

Stress Analysis in Social Media using ML Algorithms

Abstract: As the world is advancing towards building a completely digitalized environment with the lifestyle of every individual being immensely affected, it is facile for each to manifest their perspectives and actions on social media platforms. However, such kind of conduct affects the mental health of a person as virtual socialization hinders authentic individualization. Feelings of inadequacy, resentment, and seclusion cynically affect the mind and exacerbate the symptoms of depression, anxiety, and stress. A profound knowledge for ascertaining stress caused due to social media is imperative to avoid unnecessary repercussions. In this proposed method, supervised machine learning algorithms are applied on a dataset of lengthy multi-domain social media data for identifying stress from five different categories of Reddit communities by analyzing the posts when it does not explicitly contain specific keywords such as “stress” or “tension”. Two textual-based featuring methods such as BERT and TF-IDF are used along with machine learning classifiers to adjudge the sentiment of the social media post and categorize them into ‘stress' and ‘non-stress. Exploratory results exemplify that knowledge-enabled BERT is an exceptional solution for sentiment analysis research. The Random Forest classifier achieved the highest accuracy of 75.80% with the BERT fine-tuned model. Furthermore, distinct evaluation metrics, namely, precision, recall, specificity, and F1-score of the models are estimated to single out the ultimate model.