Improved Classification of Histopathological images using the feature fusion of Thepade sorted block truncation code and Niblack thresholding

Histopathology is the study of disease-affected tissues, and it is particularly helpful in diagnosis and figuring out how severe and rapidly a disease is spreading. It also demonstrates how to recognize a variety of human tissues and analyze the alterations brought on by sickness. Only through histopathological pictures can a specific collection of disease characteristics, such as lymphocytic infiltration of malignancy, be determined. The "gold standard" for diagnosing practically all cancer forms is a histopathological picture. Diagnosis and prognosis of cancer at an early stage are essential for treatment, which has become a requirement in cancer research. The importance and advantages of classification of cancer patients into more-risk or less-risk divisions have motivated many researchers to study and improve the application of machine learning (ML) methods. It would be interesting to explore the performance of multiple ML algorithms in classifying these histopathological images. Something crucial in this field of ML for differentiating images is feature extraction. Features are the distinctive identifiers of an image that provide a brief about it. Features are drawn out for discrimination between the images using a variety of handcrafted algorithms. This paper presents a fusion of features extracted with Thepade sorted block truncation code (TSBTC) and Niblack thresholding algorithm for the classification of histopathological images. The experimental validation is done using 960 images present in the Kimiapath-960 dataset of histopathological images with the help of performance metrics like sensitivity, specificity and accuracy. Better performance is observed by an ensemble of TSBTC N-ary and Niblack's thresholding features as 97.92% of accuracy in 10-fold cross-validation.