Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

作     者：Imran Ahmed Humaira Sardar Hanan Aljuaid Fakhri Alam Khan Muhammad Nawaz Adnan Awais 

作者机构：Center of Excellence in Information TechnologyInstitute of Management SciencesPeshawarPakistan Department of Computer SciencesCollege of Computer and Information SciencesPrincess Nourah Bint Abdulrahman University(PNU)RiyadhSaudi Arabia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2021年第69卷第12期

页      面：3365-3381页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This research was funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program 

主　　题：Convolutional neural network histopathological image classification osteosarcoma computer-aided diagnosis 

摘      要：Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality *** diagnosis may increase the chances of treatment and survival however the process is time-consuming(reliability and complexity involved to extract the hand-crafted features)and largely depends on pathologists’*** Neural Network(CNN—an end-to-end model)is known to be an alternative to overcome the aforesaid ***,this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet(a high-class imbalanced dataset).Though,during training,class-imbalanced data can negatively affect the performance of ***,an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization *** this process,a hierarchical CNN model is designed,in which the former model is non-regularized(due to dense architecture)and the later one is regularized,specifically designed for small histopathology ***,the regularized model is integrated with CNN’s basic architecture to reduce *** results demonstrate that oversampling might be an effective way to address the imbalanced class problem during *** training and testing accuracies of the non-regularized CNN model are 98%&78%with an imbalanced dataset and 96%&81%with a balanced dataset,*** regularized CNN model training and testing accuracies are 84%&75%for an imbalanced dataset and 87%&86%for a balanced dataset.