MIT-World Peace University (MIT-WPU) is a top educational institute located in Pune, India. It is recognized within&nbsp;UGC under Government of Maharashtra Act No. XXXV 2017.&nbsp;

It is part of the MIT group of institutions (established in 1983) which covers 10+ campuses across India and has more than 50,000+ students enrolled across various disciplines at a given point of time. The institute houses 15 schools ranging from Engineering &amp; Technology, Design, Pharmacy, Commerce, Governance among others. It offers undergraduate, postgraduate and PhD programs among different disciplines.&nbsp;

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MIT World Peace University

Currently, lung cancer alone causes around 20% of all mortalities in cancer. In 2018 alone, 1.8 million deaths were accounted. Lung cancer does not show any prominent symptoms. The survival rate of affected persons can be enhanced by early detection of up to five years. Radiologists examine the chest CT scans slice wise for malignancy detection. The potential challenge in the prediction of the lung nodules is the variation in size, shape, the location of nodules, and the existence of nodules resembling objects. A manual detection is cumbersome and requires a high degree of skill and precision. Researchers throughout the world are currently working on developing a computer-aided approach to assist the radiologists. This study is intended to explore the different techniques for nodule detection, classification, and False Acceptance Reduction (FAR). This paper summarizes the advancements in feature-based and neural network based techniques along with their limitations and required further investigations. This study also highlights and compares a few evaluation metrics used for the assessment of the current methods. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2021.

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Advances in Intelligent Systems and Computing

Pulmonary nodule detection and false acceptance reduction: Review

This paper proposes a biometric authentication system based on feature level fusion of face and fingerprint modalities. The proposed method utilizes Gabor filter bank with two scales and eight orientations, to extract directional features from source data. Usage of a small set of Gabor filters typically reduces the system processing time. To introduce a good discriminating ability and to avoid curse of dimensionality in feature set, we have used Principal Component Analysis (PCA) +Linear Discriminant Analysis (LDA) framework. The framework enables us to use only 39 features as input to classifier stage of the system. Distance classifiers are employed to authenticate a person based on the distance between input image and stored database templates. Experimental results showcase the advantages of feature level fusion over a uni-modal framework. The system achieves recognition accuracy up to 99.25%. The experiments have been carried out on ORL face database and FVC2002 fingerprint database. © 2013 IEEE.