Preeti Aggarwal

Semantic and content-based medical image retrieval for lung cancer diagnosis with the inclusion of expert knowledge and proven pathology

This paper involves the analysis and experimentation of chest CT scan data for the detection and diagnosis of lung cancer. In lung cancer computer-aided diagnosis (CAD) systems, having an accurate ground truth is critical and time consuming. The contribution of this work include the development of lung nodule database with proven pathology using content based image retrieval (CBIR) and algorithms for detection and classification of nodules. A study and analysis of 246 patients have been carried out for the detection of benign, malignant as well as metastasis nodules. The whole research work has been carried out using Lung Image Database Consortium (LIDC) database by National Cancer Institute (NCI), USA and achieved an average precision of 92.8% and mean average precision of 82% at recall 0.1. Finally, the validations have been carried out with the PGIMER, Chandigarh test cases and achieved an average precision of 88%. Experimental studies show that the proposed parameters and analysis improves the semantic performance while reducing the computational complexity, reading and analysing all slices by physicians and retrieval time.

Patient-Wise Versus Nodule-Wise Classification of Annotated Pulmonary Nodules using Pathologically Confirmed Cases

This paper presents a novel framework for combining well known shape, texture, size and resolution informatics descriptor of solitary pulmonary nodules (SPNs) detected using CT scan. The proposed methodology evaluates the performance of classifier in differentiating benign, malignant as well as metastasis SPNs with 246 chests CT scan of patients. Both patient-wise as well as nodule-wise available diagnostic report of 80 patients was used in differentiating the SPNs and the results were compared. For patient-wise data, generated a model with efficiency of 62.55% with labeled nodules and using semi-supervised approach, labels of rest of the unknown nodules were predicted and finally classification accuracy of 82.32% is achieved with all labeled nodules. For nodule-wise data, ground truth database of labeled nodules is expanded from a very small ground truth using content based image retrieval (CBIR) method and achieved a precision of 98%. Proposed methodology not only avoids unnecessary biopsies but also efficiently label unknown nodules using pre-diagnosed cases which can certainly help the physicians in diagnosis.

Content Based Image Retrieval Approach in Creating an Effective Feature Index for Lung Nodule Detection with the Inclusion of Expert Knowledge and Proven Pathology

The paper investigates four major issues in the active field of lung computer aided diagnosis (CAD) using content-based image retrieval (CBIR), which are: creating an efficient feature index for lung nodules for similarity measures, database creation of nodules with proven pathology, robust CBIR system and present a self-diagnosing environment to assist the physician in taking the right decision at right time. The results definitely improves the radiologists performance of detecting suspicious nodules based on the ground truth prepared. CBIR has been implemented to expand the small ground truth of 17 nodules to ground truth of 114 nodules based on available biopsy report. Nine out of 83 different extracted features have been considered as the best discriminating features to classify the lung nodules in three classes: Malignant, Benign and Metastasis. LIDC database has been analysed and achieved an average precision of 92.8% , mean average precision (MAP) of 82% at recall 0.1 and an average precision of 88% with PGIMER, Chandigarh. Results in this paper also indicate that the unnecessary biopsies can be avoided as the results are having few number of false positives which can directly increase the specificity of the proposed research.

Correlation between Biopsy Confirmed Cases and Radiologist's Annotations in the Detection of Lung Nodules by Expanding the Diagnostic Database Using Content Based Image Retrieval

In lung cancer computer-aided diagnosis CAD systems, having an accurate and available ground truth is critical and time consuming. In this study, we have explored Lung Image Database Consortium LIDC database containing pulmonary computed tomography CT scans, and we have implemented content-based image retrieval CBIR approach to exploit the limited amount of diagnostically labeled data in order to annotate unlabeled images with diagnoses. By applying CBIR method iteratively and using pathologically confirmed cases, we expand the set of diagnosed data available for CAD systems from 17 nodules to 121 nodules. We evaluated the method by implementing a CAD system that uses various combinations of lung nodule sets as queries and retrieves similar nodules from the diagnostically labeled dataset. In calculating the precision of this system Diagnosed dataset and computer-predicted malignancy data are used as ground truth for the undiagnosed query nodules. Our results indicate that CBIR expansion is an effective method for labeling undiagnosed images in order to improve the performance of CAD systems. It also indicated that little knowledge of biopsy confirmed cases not only assist the physicians as second opinion to mark the undiagnosed cases and avoid unnecessary biopsies too.

Comparative Analysis of Machine Learning Algorithms for Breast Cancer Prognosis

In the past few years, gynecological cancers have taken their toll on women’s health. Breast cancer is the major cause of cancer death followed by ovarian cancer and others. In this paper, we aim at finding the cancer status of the patient, whether it is benign or malignant. Data is collected from WISCONSIN dataset of UCI machine learning Repository. The dataset includes the cases of patients who are at risk of developing the cancer or have redeveloped the cancer. Different attribute selection techniques are applied on the data set. Further, different classification algorithms are used to compare and analyze the results.

Extraction and Sequencing of Keywords from Twitter

Social media has been the game changer of this generation much like telephony was for the previous. The amount of information available on this platform is huge. This information if extracted and analyzed, can be an immensely helpful source of news and latest developments around the world. As a source and sink of information, it is much faster than traditional news channels and media platforms. This paper uses Twitter data to extract keywords and then sequence them to give useful information. Keywords are extracted from graph constructed from users’ posts by heaviest k-subgraph problem. We then proposed a method to sequence extracted keywords in a particular order to get some meaningful information by using Edmonds’ algorithm.