A dual stage AlexNet-HHO-DrpXLM archetype for an effective feature extraction, classification and prediction of liver cancer based on histopathology images. (August 2022)

• Record Type: Journal Article
• Authors: Sabitha, P.; Meeragandhi, G.
• Is Part Of: Biomedical signal processing and control. Volume 77(2022)
• Publication Date: 2022-08
• Subjects: Hepatocellular carcinoma -- Horse herd optimization -- Haematoxylin and Eosin (H&E) images -- KMC liver dataset -- Dropout extreme learning machine; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.103833
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: More number of people affected by the liver cancer globally. HCC is one types of liver cancer that mainly affects the adults and it leads to death. Initially, the developed dual stage model is utilized to classify the histopathological images. Histopathology images are obtained from the standard datasets, like KMC liver dataset. DrpXLM classifier is used for classifying the Hepatocellular carcinoma images. Abstract: Nowadays, more number of people affected by the liver cancer globally. Hepatocellular carcinoma (HCC) is one types of liver cancer that mainly affects the adults and it leads to death. Hepatocellular carcinoma disease diagnosis in an early stage is very necessary in the medical industry for reducing the death rate. However, the existing approaches have more difficulties to obtain the accurate results due to its high error pruning, time consuming and provided very low accuracy for classifying the cancer. So this work proposed innovative dual stage AlexNet- Horse Herd Optimization- Dropout Extreme Learning Machine (AlexNet-HHO-DrpXLM) architecture for classifying the histopathological haematoxylin and eosin (H&E) images. Initially, the developed dual stage model is utilized to classify the histopathological images. Moreover, the histopathology images are obtained from the standard datasets, like KMC liver dataset, which are trained to the AlexNet model. Subsequently, the Dropout Extreme Learning Machine (DrpXLM) classifier is used for classifying the Hepatocellular carcinoma images and the performance is improved by Horse Herd Optimization (HHO) mechanism. The performance of this work is compared with recent existing methods in terms of accuracy, precision, sensitivity, specificity and recall. Hence, the proposed method attains 21.43%, 27.50%, 22.89%, 26.42%, 3.58% and 2.07%higher accuracy values for malignant classification and 25.70%, 37.48%, 9.99%, 33.32%, 25.70% and 7.31% higher accuracy values for benign. The comparison results prove that the efficiency of the proposed AlexNet-HHO-DrpXLM method is more efficient than the other existing methods.