Automated Detection of Acute Leukemia using K-mean Clustering Algorithm

Leukemia is a hematologic cancer which develops in blood tissue and triggers rapid production of immature and abnormal shaped white blood cells. Based on statistics it is found that the leukemia is one of the leading causes of death in men and women alike. Microscopic examination of blood sample or bone marrow smear is the most effective technique for diagnosis of leukemia. Pathologists analyze microscopic samples to make diagnostic assessments on the basis of characteristic cell features. Recently, computerized methods for cancer detection have been explored towards minimizing human intervention and providing accurate clinical information. This paper presents an algorithm for automated image based acute leukemia detection systems. The method implemented uses basic enhancement, morphology, filtering and segmenting technique to extract region of interest using k-means clustering algorithm. The proposed algorithm achieved an accuracy of 92.8% and is tested with Nearest Neighbor (KNN) and Naive Bayes Classifier on the data-set of 60 samples.Comment: Presented in ICCCCS 201

A Framework for Computerized Adaptive Assessment based on Trajectory Driven Pedagogy Implemented in an Engineering Course

Engineering education needs to be flexible with the changing technology, and it must blend traditional and new teaching pedagogy for the overall knowledge creation in the students. A survey of prevalent experiential learning methods has shown tremendous potential to improve engineering students' learning. However, existing experiential learning methods are hard to integrate with current teaching-learning process at Amity University, Uttar Pradesh, Lucknow Campus, India. A pilot study conducted during Power plant Instrumentation taught in the seventh semester of the Electrical and Electronics undergraduate program balances the current teaching method with the proposed Trajectory -driven pedagogy as an alternative teaching pedagogy. A trajectory driven computerized adaptive assessment procedure for teaching has been proposed in this paper. The system follows a trajectory of courses to generate the subsequent questions from the vast database of questions. A sequence of questions is guided by Concept Map which represents the questions from three courses in a hierarchical manner. Analysis of students' assessments shows that the proposed methodology could is accurate for quantitative measurement of the course learning outcomes in a summative assessment. &nbsp

Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis

Computer Aided Diagnosis has emerged as an indispensible technique for validating the opinion of radiologists in CT interpretation. This paper presents a deep 3D Convolutional Neural Network (CNN) architecture for automated CT scan-based lung cancer detection system. It utilizes three dimensional spatial information to learn highly discriminative 3 dimensional features instead of 2D features like texture or geometric shape whick need to be generated manually. The proposed deep learning method automatically extracts the 3D features on the basis of spatio-temporal statistics.The developed model is end-to-end and is able to predict malignancy of each voxel for given input scan. Simulation results demonstrate the effectiveness of proposed 3D CNN network for classification of lung nodule in-spite of limited computational capabilities.Comment: Initial draft of PAPER Presented at IRSCNS 2018 , Goa , India final version available at Mishra S., Chaudhary N.K., Asthana P., Kumar A. (2019) Deep 3D Convolutional Neural Network for Automated Lung Cancer Diagnosis. In: Peng SL., Dey N., Bundele M. (eds) Computing and Network Sustainability. Lecture Notes in Networks and Systems, vol 75. Springer, Singapor