Call for Chapters: AI-Driven Histological Tissue Classification in Veterinary Sciences

Editors

AYHAN AKGÜN, Iğdır University, Turkey

Call for Chapters

Proposals Submission Deadline: May 24, 2026
 Full Chapters Due: September 6, 2026
 Submission Date: September 6, 2026

Introduction

Recent advances in artificial intelligence (AI) have significantly transformed various scientific fields, including veterinary sciences. Among these advances, AI-assisted image analysis of histological tissues has emerged as a powerful tool for improving the accuracy, efficiency, and objectivity of histological tissue classification. Histopathological assessment remains a fundamental element in the diagnosis of animal diseases; however, traditional methods rely heavily on the subjective interpretations of experts, which can lead to inter-observer variability and time-consuming workflows. The integration of machine learning and deep learning algorithms into histological analysis offers new opportunities to overcome these limitations. AI systems, particularly convolutional neural networks (CNNs), have demonstrated remarkable performance in recognizing complex tissue patterns, distinguishing between normal and pathological structures, and supporting diagnostic decision-making processes. These technologies enable automated feature extraction and classification, reducing human error and increasing reproducibility. In veterinary sciences, where species diversity and limited annotated datasets pose additional challenges, AI-driven approaches provide a promising framework for enhancing diagnostic precision and standardization. This study aims to explore the applications, benefits, and current limitations of AI-based histological tissue classification, highlighting its potential to revolutionize veterinary pathology and contribute to more accurate and rapid disease diagnosis.

Objective

The primary objective of this book is to provide a comprehensive and interdisciplinary understanding of AI-driven histological tissue classification within the context of veterinary sciences. In line with this overarching goal, the book aims to: Present the fundamental principles of histology and veterinary pathology to establish a solid conceptual background for readers from diverse disciplines. Introduce core concepts of artificial intelligence, machine learning, and deep learning, with a focus on their application in biomedical image analysis. Explore state-of-the-art AI techniques, including convolutional neural networks (CNNs), used in histological image classification and pattern recognition. Demonstrate practical applications of AI in veterinary histopathology through case studies and real-world examples. Evaluate the advantages, limitations, and challenges of integrating AI technologies into veterinary diagnostic workflows. Address issues related to dataset preparation, annotation, and variability across animal species. Highlight ethical considerations, data privacy, and the role of AI in supporting—not replacing—expert decision-making. Provide guidance for researchers and clinicians on implementing AI-based systems in laboratory and clinical settings. Encourage interdisciplinary collaboration between veterinarians, pathologists, data scientists, and engineers. Offer insights into future trends and emerging technologies that may shape the evolution of veterinary diagnostic pathology.

Target Audience

This book is intended for a broad and interdisciplinary audience interested in the intersection of veterinary sciences and artificial intelligence. It is particularly designed for: Veterinary students and postgraduate researchers seeking to understand modern approaches in histopathology and diagnostic technologies. Veterinary pathologists and clinicians aiming to integrate AI-based tools into their diagnostic workflows. Researchers in biomedical sciences working on histology, pathology, and image analysis. Data scientists, bioinformaticians, and engineers interested in applying machine learning and deep learning techniques to medical imaging. Academicians and educators who wish to incorporate emerging technologies into veterinary and life sciences curricula. Professionals in related fields such as comparative pathology, laboratory animal science, and translational medicine. The book is structured to be accessible to readers with varying levels of expertise, providing both fundamental knowledge and advanced insights into AI-driven histological tissue classification.

Recommended Topics

The following topics are proposed to ensure a comprehensive and structured coverage of AI-driven histological tissue classification in veterinary sciences: Digital Pathology and Whole Slide Imaging (WSI) Technologies Introduction to Artificial Intelligence in Histology: Basic Concepts and Terminology Fundamentals of Machine Learning and Deep Learning in Histology Data Collection and Labeling Processes in Histological Images Segmentation Techniques in Histological Images (U-Net, etc.) Tissue Classification with Convolutional Neural Networks (CNN) in Histology Deep Learning Architectures in Histology (ResNet, VGG, EfficientNet, etc.) Tumor and Lesion Detection in Histological Images Artificial Intelligence Applications in Veterinary Oncology Histological Analysis of Zoonotic Diseases and Artificial Intelligence Comparative Pathology and Interspecies Analyses Model Performance Evaluation in Histology (Accuracy, Precision, Recall, F1 Score) Explainable Artificial Intelligence (AI) and Clinical Reliability in Histology Large Data (Big Data) and Histopathological Data Management Artificial Intelligence and Digital Histology in Veterinary Education Integration of Artificial Intelligence and Histology in the Future

Submission Procedure

Researchers and practitioners are invited to submit on or before May 24, 2026, a chapter proposal of 1,000 to 2,000 words clearly explaining the mission and concerns of his or her proposed chapter. Authors will be notified by June 7, 2026 about the status of their proposals and sent chapter guidelines.Full chapters of a minimum of 10,000 words (word count includes references and related readings) are expected to be submitted by September 6, 2026, and all interested authors must consult the guidelines for manuscript submissions at https://www.igi-global.com/publish/contributor-resources/before-you-write/ prior to submission. All submitted chapters will be reviewed on a double-anonymized review basis. Contributors may also be requested to serve as reviewers for this project.

Note: There are no submission or acceptance fees for manuscripts submitted to this book publication, AI-Driven Histological Tissue Classification in Veterinary Sciences. All manuscripts are accepted based on a double-anonymized peer review editorial process.

All proposals should be submitted through the eEditorial Discovery® online submission manager.

Publisher

This book is scheduled to be published by IGI Global Scientific Publishing, an international academic publisher of the "Information Science Reference", "Medical Information Science Reference", "Business Science Reference", and "Engineering Science Reference" imprints. IGI Global Scientific Publishing specializes in publishing reference books, scholarly journals, and electronic databases featuring academic research on a variety of innovative topic areas including, but not limited to, education, social science, medicine and healthcare, business and management, information science and technology, engineering, public administration, library and information science, media and communication studies, and environmental science. For additional information regarding the publisher, please visit https://www.igi-global.com. This publication is anticipated to be released in 2027.

Indexing Information for Prospective Authors

IGI Global Scientific Publishing meets the criteria for inclusion in major indexing services such as Scopus; however, it is important to note that all indexing decisions are made independently by these services. IGI Global Scientific Publishing books are selectively indexed by the indexing organization after publication. Indexing cannot be guaranteed for any book prior to publication, and the indexing organization has complete control over the final selection and timeline.

Important Dates

May 24, 2026: Proposal Submission Deadline
June 7, 2026: Notification of Acceptance
September 6, 2026: Full Chapter Submission
November 8, 2026: Review Results Returned
December 20, 2026: Final Acceptance Notification
January 3, 2027: Final Chapter Submission

Inquiries

AYHAN AKGÜN
Iğdır University
ayhan.akgun@igdir.edu.tr
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