M. Midhila

Bio: M. Midhila is an academic researcher from Amrita Vishwa Vidyapeetham. The author has contributed to research in topics: Medical imaging & Feature extraction. The author has an hindex of 2, co-authored 2 publications receiving 9 citations.

Tensor Flow Based Analysis and Classification of Liver Disorders from Ultrasonography Images

Abstract: In the field of medical imaging, Ultrasonography is a popular and most frequently used diagnostic tool owing to its hazard-free, non–invasive and the cost effective nature. Liver being the largest and vital organ in the human body, liver disorders are treated very important and initial detection of the disorder is made using ultrasound imaging by the radiologists that leads to additional biopsies for confirmation, if necessary. This work focusses on the automated classification of nine types of both focal and diffused liver disorders using ultrasound images. A deep convolutional neural network architecture codenamed Inception is used. The technique achieves a new state for classification and detection of liver disease. The disease is predicted based on the score obtained as a result of training. The classification is achieved using tensor flow and it outputs the predicted labels and the corresponding scores. The method achieves reasonable accuracy using the trained model.

A study of the phases of classification of liver diseases from ultrasound images and gray level difference weights based segmentation

Abstract: This paper presents a study of the state of the art techniques applied to computer based analysis and classification of liver diseases from ultrasound images. The diseased portions from the ultrasound images are analyzed and categorized using techniques such as Despeckling, Segmentation, Feature extraction and Classification. Automatic segmentation of ultrasound images is complicated due to the fact that the image may include other organs which are close to the liver, irregular structure of disease, poor quality of image, lack of color cues, and lack of definite boundaries and presence of noise. This work makes a study of different techniques used in the different phases of biomedical liver ultrasound processing such as noise removal, segmentation, Feature Extraction and classification. This work also presents the segmentation results obtained using Gray Level Difference Weights Method on 10 types of liver diseases from ultrasound images.

Understanding tourists’ urban images with geotagged photos using convolutional neural networks

Abstract: This study aims to track down representative images and elements of sightseeing attractions by analyzing the photos uploaded on Flickr by Seoul tourists with the image mining technique. For this purpose, we crawled the photos uploaded on Flickr and classified users into residents and tourists; drew 11 region of attractions (RoA) in Seoul by analyzing the spatial density of the photos; classified the photos into 1000 categories and then 14 categories by grouping 1000 categories by utilizing Inception V3 model; analyzed the characteristics of the photo image by RoA. Key findings of this study are that tourists are interested in old palaces, historical monuments, stores, food, etc. and those key elements are distinguished from the major sightseeing attractions in Seoul. More specifically, tourists are more interested in palaces and cultural assets in Jongno and Namsan, food and restaurants in Shinchon, Hongdae, Itaewon, Yeouido, Garosu-gil, and Apgujeong, war monuments or specific artifacts in War Memorial and the National Museum of Korea, facilities, temples, and pictures of cultural properties in Samsung Station, and toyshops in Jamsil. This study is meaningful in three folds: first, it tries to analyze urban image through the photos posted on SNS by tourists. Second, it uses deep learning technique to analyze the photos. Third, it classifies and analyzes the whole photos posted by Seoul tourists while most of other researches focus on only specific objects. However, this study has a limitation because the Inception v3 model which has been used in this research is a pre-trained model created by training the ImageNet data. In future research, it is necessary to classify photo categories according to the purpose of tourism and retrain the model by creating new training data set focusing on elements of Korea.

Supervised machine learning based liver disease prediction approach with LASSO feature selection

Abstract: In this contemporary era, the uses of machine learning techniques are increasing rapidly in the field of medical science for detecting various diseases such as liver disease (LD). Around the globe, a large number of people die because of this deadly disease. By diagnosing the disease in a primary stage, early treatment can be helpful to cure the patient. In this research paper, a method is proposed to diagnose the LD using supervised machine learning classification algorithms, namely logistic regression, decision tree, random forest, AdaBoost, KNN, linear discriminant analysis, gradient boosting and support vector machine (SVM). We also deployed a least absolute shrinkage and selection operator (LASSO) feature selection technique on our taken dataset to suggest the most highly correlated attributes of LD. The predictions with 10 fold cross-validation (CV) made by the algorithms are tested in terms of accuracy, sensitivity, precision and f1-score values to forecast the disease. It is observed that the decision tree algorithm has the best performance score where accuracy, precision, sensitivity and f1-score values are 94.295%, 92%, 99% and 96% respectively with the inclusion of LASSO. Furthermore, a comparison with recent studies is shown to prove the significance of the proposed system.

A TensorFlow Approach to Data Analysis for Time Series Forecasting in the Energy-Efficiency Realm

Abstract: Thanks to advances in smart metering devices (SM), the electricity sector is undergoing a series of changes, among which it is worth highlighting the ability to control the response to all events that occur in the electricity grid with the intention of making it more smart. Predicting electricity consumption data is a key factor for the energy sector in order to create a completely intelligent electricity grid that optimizes consumption and forecasts future energy needs. However, it is currently not enough to give a prediction of energy consumption (EC), but it is also necessary to give the prediction as fast as possible so that the grid can operate in the shortest possible time. An approach for developing EC prediction systems is introduced here by the use of artificial neural networks (ANN). Differently from other research studies on the subject, a divide-and-conquer strategy is used so that the target system’s execution switches from one to another specialized small models that forecast the EC of a building within the time range of one hour. By simultaneously processing a large amount of data and models, a consequence of implementing them in parallel with TensorFlow on GPUs, the training procedure proposed here increases the performance of the classic time series prediction methods, which are based on ANN. Leveraging the latest generation of ANN techniques and new GPU-based architectures, correct EC predictions can be obtained and, as the experimentation carried out in this work shows, such predictions can be obtained quickly. The obtained results in this study show a promising way for speeding up big data processing of building’s monitoring data to achieve energy efficiency.

Tensor Flow Based Analysis and Classification of Liver Disorders from Ultrasonography Images

Abstract: In the field of medical imaging, Ultrasonography is a popular and most frequently used diagnostic tool owing to its hazard-free, non–invasive and the cost effective nature. Liver being the largest and vital organ in the human body, liver disorders are treated very important and initial detection of the disorder is made using ultrasound imaging by the radiologists that leads to additional biopsies for confirmation, if necessary. This work focusses on the automated classification of nine types of both focal and diffused liver disorders using ultrasound images. A deep convolutional neural network architecture codenamed Inception is used. The technique achieves a new state for classification and detection of liver disease. The disease is predicted based on the score obtained as a result of training. The classification is achieved using tensor flow and it outputs the predicted labels and the corresponding scores. The method achieves reasonable accuracy using the trained model.

Artificial intelligence in computer-aided diagnosis of abdomen diseases

Abstract: The abdomen contains many organs, along with a range of abdominal diseases that require accurate diagnosis. Clinical imaging plays a crucial role in abdominal disease diagnosis, prognosis, and treatment assessment. For decades, physicists have focused on innovation in terms of imaging techniques, assisting radiologists to improve abdominal diseases detection and diagnosis. Consequently, many new imaging methods for abdominal organs have been developed, including computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), X-ray, endoscopy, and ultrasound (McAuliffe et al., 2001; Dhawan et al., 2011). Each imaging method has its own unique features. For example, abdominal MRI can achieve the most comprehensive assessment of abdominal lesions, such as the liver, kidney and prostate (Yang et al., 2017; Wang et al., 2018; Gao et al., 2017) mainly due to its diversiform sequences, including structural imaging such as T1 weighted imaging and T2 weighted imaging, as well as diffusion imaging, perfusion imaging, arterial spin labeling and other functional imaging that reflect different functional changes. Recently, the success of machine learning (ML) in computer vision has attracted the attention of the medical image analysis community. The use of ML has been increasing rapidly in the medical field including radiomics and medical image analysis. Progress of artificial intelligence in medical image analysis