Classification of human carcinoma cells using multispectral imagery

Abstract

In this paper, we present a technique for automatically classifying human carcinoma cell images using textural features. An image dataset containing microscopy biopsy images from different patients for 14 distinct cancer cell line type is studied. The images are captured using a RGB camera attached to an inverted microscopy device. Texture based Gabor features are extracted from multispectral input images. SVM classifier is used to generate a descriptive model for the purpose of cell line classification. The experimental results depict satisfactory performance, and the proposed method is versatile for various microscopy magnification options.

Performing Automatic Identification and Staging of Urothelial Carcinoma in Bladder Cancer Patients Using a Hybrid Deep-Machine Learning Approach

“…Ref. [ 19 ] utilized Gabor features to perform carcinoma cell classification from biopsy images associated with 14 distinct cancer types, scanned from 14 different patients. An SVM classifier with a radial basis function (RBF) kernel was subsequently trained on these features.…”