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Artificial Intelligence in Histopathological Image Analysis

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Dr. Mohammed M. Abdelsamea

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School of Computing and Digital Technology, Birmingham City University, Birmingham, UK
Interests: image processing; deep learning; machine learning; medical image analysis

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) is the backbone of histopathological image analysis systems, which is one of the most important technologies used in the field of histopathology. In recent years, deep learning techniques have been used to learn the local hierarchical levels of representation from histopathological images in various ways (including via semi-supervised and self-supervised methods). These methods comprise a variety of promising ways to extract relevant diagnostic and prognostic features. Due to the requirements for contextual information, context-aware learning methods, such as transformer-based and graph-based models, have been adopted to provide accurate diagnoses with which to more effectively manage patients and customise treatment plans. Such models can help researchers to discover new digital biomarkers and may constitue a crucial step toward closing the translation gap between AI and clinical practice. Nevertheless, interpretable methods are still needed to understand the underlying mechanism behind the AI’s decisions. This will not only increase users' trust in these technologies but also allow the wide use of algorithms trained for specific tasks in a way akin to what occurs human approaches. The purpose of this Special Issue is to discuss recent advances in the use of state-of-the-art AI systems for histopathological image analysis, the challenges and opportunities inherent in the use of these methods, and their reliability in the field.

Dr. Mohammed M. Abdelsamea
Guest Editor

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25 pages, 6398 KiB

Open AccessArticle

Adapted Deep Ensemble Learning-Based Voting Classifier for Osteosarcoma Cancer Classification

by Md. Abul Ala Walid, Swarnali Mollick, Pintu Chandra Shill, Mrinal Kanti Baowaly, Md. Rabiul Islam, Md. Martuza Ahamad, Manal A. Othman and Md Abdus Samad

Diagnostics 2023, 13(19), 3155; https://doi.org/10.3390/diagnostics13193155 - 09 Oct 2023

Cited by 3 | Viewed by 1562

Abstract

The study utilizes osteosarcoma hematoxylin and the Eosin-stained image dataset, which is unevenly dispersed, and it raises concerns about the potential impact on the overall performance and reliability of any analyses or models derived from the dataset. In this study, a deep-learning-based convolution neural network (CNN) and adapted heterogeneous ensemble-learning-based voting classifier have been proposed to classify osteosarcoma. The proposed methods can also resolve the issue and develop unbiased learning models by introducing an evenly distributed training dataset. Data augmentation is employed to boost the generalization abilities. Six different pre-trained CNN models, namely MobileNetV1, Mo-bileNetV2, ResNetV250, InceptionV2, EfficientNetV2B0, and NasNetMobile, are applied and evaluated in frozen and fine-tuned-based phases. In addition, a novel CNN model and adapted heterogeneous ensemble-learning-based voting classifier developed from the proposed CNN model, fine-tuned NasNetMobile model, and fine-tuned Efficient-NetV2B0 model are also introduced to classify osteosarcoma. The proposed CNN model outperforms other pre-trained models. The Kappa score obtained from the proposed CNN model is 93.09%. Notably, the proposed voting classifier attains the highest Kappa score of 96.50% and outperforms all other models. The findings of this study have practical implications in telemedicine, mobile healthcare systems, and as a supportive tool for medical professionals. Full article

(This article belongs to the Special Issue Artificial Intelligence in Histopathological Image Analysis)

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33 pages, 6761 KiB

Open AccessArticle

Hybrid Models Based on Fusion Features of a CNN and Handcrafted Features for Accurate Histopathological Image Analysis for Diagnosing Malignant Lymphomas

by Mohammed Hamdi, Ebrahim Mohammed Senan, Mukti E. Jadhav, Fekry Olayah, Bakri Awaji and Khaled M. Alalayah

Diagnostics 2023, 13(13), 2258; https://doi.org/10.3390/diagnostics13132258 - 04 Jul 2023

Cited by 2 | Viewed by 1497

Abstract

Malignant lymphoma is one of the most severe types of disease that leads to death as a result of exposure of lymphocytes to malignant tumors. The transformation of cells from indolent B-cell lymphoma to B-cell lymphoma (DBCL) is life-threatening. Biopsies taken from the patient are the gold standard for lymphoma analysis. Glass slides under a microscope are converted into whole slide images (WSI) to be analyzed by AI techniques through biomedical image processing. Because of the multiplicity of types of malignant lymphomas, manual diagnosis by pathologists is difficult, tedious, and subject to disagreement among physicians. The importance of artificial intelligence (AI) in the early diagnosis of malignant lymphoma is significant and has revolutionized the field of oncology. The use of AI in the early diagnosis of malignant lymphoma offers numerous benefits, including improved accuracy, faster diagnosis, and risk stratification. This study developed several strategies based on hybrid systems to analyze histopathological images of malignant lymphomas. For all proposed models, the images and extraction of malignant lymphocytes were optimized by the gradient vector flow (GVF) algorithm. The first strategy for diagnosing malignant lymphoma images relied on a hybrid system between three types of deep learning (DL) networks, XGBoost algorithms, and decision tree (DT) algorithms based on the GVF algorithm. The second strategy for diagnosing malignant lymphoma images was based on fusing the features of the MobileNet-VGG16, VGG16-AlexNet, and MobileNet-AlexNet models and classifying them by XGBoost and DT algorithms based on the ant colony optimization (ACO) algorithm. The color, shape, and texture features, which are called handcrafted features, were extracted by four traditional feature extraction algorithms. Because of the similarity in the biological characteristics of early-stage malignant lymphomas, the features of the fused MobileNet-VGG16, VGG16-AlexNet, and MobileNet-AlexNet models were combined with the handcrafted features and classified by the XGBoost and DT algorithms based on the ACO algorithm. We concluded that the performance of the two networks XGBoost and DT, with fused features between DL networks and handcrafted, achieved the best performance. The XGBoost network based on the fused features of MobileNet-VGG16 and handcrafted features resulted in an AUC of 99.43%, accuracy of 99.8%, precision of 99.77%, sensitivity of 99.7%, and specificity of 99.8%. This highlights the significant role of AI in the early diagnosis of malignant lymphoma, offering improved accuracy, expedited diagnosis, and enhanced risk stratification. This study highlights leveraging AI techniques and biomedical image processing; the analysis of whole slide images (WSI) converted from biopsies allows for improved accuracy, faster diagnosis, and risk stratification. The developed strategies based on hybrid systems, combining deep learning networks, XGBoost and decision tree algorithms, demonstrated promising results in diagnosing malignant lymphoma images. Furthermore, the fusion of handcrafted features with features extracted from DL networks enhanced the performance of the classification models. Full article

(This article belongs to the Special Issue Artificial Intelligence in Histopathological Image Analysis)

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