Computer aided diagnosis of cancer is a field of substantial worth in current scenario since approximately 38% population of the world is suffering from the disease. The detection of cancer is based on the observation of deformation in nuclei structure using histopathology slides/images. The proposed technique utilizes nuclei localization prior to classification of histopathology images as benign and malignant. The features used for classification are an ensemble of 150 bag of visual word features, extracted from preprocessed image and 20 handcrafted features, extracted from the internal parts of nuclei using localized histopathology images. The simulation results confirm the superiority of proposed localization based cancer classification method as compared to existing methods of the domain. It has reported average classification accuracy of 95.03% on BreakHis dataset.
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Autors are thankful to the supporting team of Jawaharlal Nehru Cancer Hospital & Research Center, (JNCH&RC) Bhopal, India. Specially Smt. Asha Joshi (Chairman), Smt. Divya Parashar (CEO & Research Coordinator), Dr. K. V. Pandya (Director), Dr. Pradeep Kolekar (Medical Director) and Mr. Rakesh Joshi (Additional Director), JNCH&RC Bhopal, India, for facilitating to work with patient data for dataset preparation.
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Kurmi, Y., Chaurasia, V., Ganesh, N. et al. Microscopic images classification for cancer diagnosis. SIViP 14, 665–673 (2020). https://doi.org/10.1007/s11760-019-01584-4
- Medical imaging
- Histopathology image
- Feature extraction
- Image classification