@inproceedings{6c905c9c89ba467eb11671f32e627888,
title = "First Order Gradient Derivative Features Based Classification of Lung Lesion Using Computed Tomography Images",
abstract = "Exhaustive exploration of biomedical images is required for better diagnosis and treatment planning at low resource settings. Biomedical images possess treasured statistics and evidence which can be coupled to predict the structure or physiology of the respective part of human body. Those treasured statistical evidence used in proposed research is radiomic features. Lung lesions are annotated and radiomic features are extracted from the labelled region to perform machine learning to classify normal and abnormal region. Also radiomic features are analyzed by visualization to identify suitable feature to classify squamous cell carcinoma, adenocarcinoma and normal cases. Support vector machine classifier is used with different kernels. SVM classifier along with Sigmoid filter turned to be promising result with better precision and f1 score compared to Radical Base Filter and Polynomial kernel.",
author = "Shraddha, {G. S.} and {Deepika Rani}, {S. B.} and Nandish Siddeshappa",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 5th International Conference on Image Information Processing, ICIIP 2019 ; Conference date: 15-11-2019 Through 17-11-2019",
year = "2019",
month = nov,
doi = "10.1109/ICIIP47207.2019.8985734",
language = "English",
series = "Proceedings of the IEEE International Conference Image Information Processing",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "542--545",
editor = "Gupta, {P. K.} and Ekta Gandotra and Vipin Tyagi and Ghrera, {Satya Prakash} and Sehgal, {Vivek Kumar}",
booktitle = "2019 5th International Conference on Image Information Processing, ICIIP 2019",
address = "United States",
}