Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate.Early 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’experience.Convolutional Neural Network(CNN—an end-to-end model)is known to be an alternative to overcome the aforesaid problems.Therefore,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 CNN.Therefore,an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance.In 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 images.Moreover,the regularized model is integrated with CNN’s basic architecture to reduce overfitting.Experimental results demonstrate that oversampling might be an effective way to address the imbalanced class problem during training.The training and testing accuracies of the non-regularized CNN model are 98%&78%with an imbalanced dataset and 96%&81%with a balanced dataset,respectively.The regularized CNN model training and testing accuracies are 84%&75%for an imbalanced dataset and 87%&86%for a balanced dataset.

Ensemble of Handcrafted and Deep Learning Model for Histopathological Image Classification

Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining them.The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge.At the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data representation.With this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)model.The proposed EHCDLHIC technique initially performs Weiner filtering based noise removal technique.Once the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are extracted.For the classification process,the bidirectional gated recurrent unit(BGRU)model can be employed.At the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the work.For validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is performed.The experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.

A PROPOPSAL CONCERNING THE HISTOLOGICAL TYPING OF PRIMARY NASOPHARYNGEAL CARCINOMA

A proposal concerning the histological typing of primary nasopharyngeal carcinoma is offered in order to coincide with pathologic terms used both by Chinese and foreign pathologists and reflect the achievements in the research field of NPC. This proposal was worked out mainly basing upon the authors’ diagnostic experience gained in the past 30 years and the international criteria for tumor classification. Primary nasopharyngeal carcinoma could be classified into four major types, namely, keratinizing squamous cell carcinoma (KSCC), non- keratinizing carcinoma (NKC), adenocarcinoma (AC) and carcinomain-situ (CIS). KSCC could be graded as being well, moderately and poorly differentiated according to the amount of keratinization and intercellular bridges presented in the biopsy slide. The NKC is the most frequent type seen in the high-incidence area of NPC, and could also be subdivided into differentiated and undifferentiated variants. Actually, three grades of KSCC and two variants of NKC are a reflection of different degrees of squamous differentiation. They are consistently associated with Epstein-Barr virus (EBV) infection. There are two major categories of nasopharyngeal AC, namely, traditional and salivary-gland type. As contrasted with KSCC and NKC, nasopharyngeal AC is rarely infected with EBV. There are two subtypes of CIS, namely, squamous- and columnar-cell type. The histological typing concerning the primary nasopharyngeal carcinoma offered above is really a practical proposal and also coincided with the international usage. This proposal can be mastered easily and the authors recommend its routine use in diagnostic pathology.