Istopathology Image Colorization for Diagnosis

Abstract

This paper reviews Histopathology image colorization for diagnosis. Histopathology refers to the examination of invasive or less invasive biopsy sample by a pathologist under microscope for locating, analysing and classifying most of the diseases like cancer. The analysis of histopathological image is done manually by the pathologist to detect disease which leads to subjective diagnosis of sample and varies with level of expertise of examiner. The histopathologist meticulously evaluates tissue architecture, cellular arrangement, and anomalies in cell morphology to discern between benign and malignant conditions within the image. However, this process is labor-intensive and susceptible to both intra and inter-observer variability. To mitigate these challenges, there arises a necessity for computer-assisted image analysis, facilitating a quantitative approach to tissue diagnosis. The process which is useful to analyse the tissue. Pathologist who can observed the tissue under the microscope which has low resolution by colorization of the tissue which gives the clarity in the tissue. The actual process which can be done by the manually which would be taken the long time and accuracy rate would the low. When use this model it would give the highly accuracy rate and also take the less time to analyse the tissue. In this model we have trained the thousands of tissues which contain the non-cancer tissues and the cancer tissues, When trained with the large dataset then it would give the accuracy rate also high

Introduction

I. INTRODUCTION

Histology involves the microscopic study of cells and tissues, crucial for understanding disease development and prognosis. Pathologists examine biopsy samples by processing them into glass slides and observing them under a microscope at various magnifications. They identify morphological characteristics indicating disease presence, such as cancerous growths. Grading processes assess disease spread within tissues, guiding treatment planning based on severity.

However, manual diagnosis by pathologists is subjective and prone to variations. Quantitative assessment of histological images is crucial for objective diagnosis. With advancements in digital scanners, pathological images are now available in digital format, enabling computer-assisted image analysis. Digital image processing algorithms facilitate this analysis, aiding in computer-assisted disease diagnosis (CAD). Histopathology images when observed under the microscope the images should be appeared as the black and white colour which should not be high resolution to analyse the pathologist.

When we use the image colorization method the tissue get the high resolution of the tissue. In the tissue which contain the cancer cell they Can be identified easily. The colorization we use conventional neural network model(CNN)model which help to resize the Image . Highlight the cancer cells which are in the tissue which makes pathologist easy to analyse the tissue which may or may not contain the cancer cells in the tissue.

II. LITERATURE SERVEY

1. Progressing in the battle against cancer demands early detection, a feat achievable solely through the implementation of a highly efficient detection system .Techniques have been developed to detect breast cancer, including tissue image processing and digital pathology analysis.
2. Images are acquired by histopathology, which generally includes biopsy of the affected tissue. Tissues affected by the tumors are extracted by the pathologist and stained by H& E, which is the combination of histological stains called hematoxylin and eosin .Following this, the tissue sample undergoes microscopic examination, wherein pathologists scrutinize for cancerous cells by identifying malignant characteristics within cellular structures, such as abnormalities in nuclei morphology.
3. The acquisition of these microscopic images enables the development of computer-aided detection systems. Manual detection, being a laborious and fatiguing process, is prone to human error, particularly as cellular structures often appear irregular, random, and subject to arbitrary visual angles.
4. The primary objective is to distinguish between benign and malignant tumors, as malignant ones are cancerous and necessitate prompt treatment to mitigate and avert further complications. Essentially, this task boils down to a binary classification problem, which can be addressed through a variety of machine learning techniques.
5. Research has demonstrated that machine learning algorithms often outperform human pathologists in certain contexts. Numerous scholars have found that employing machine learning for medical image processing yields more precise results compared to the subjective diagnosis provided by a pathologist..
6. A study in Europe has been conducted by Phillips in which a set of algorithms along with breast images provided more accurate detection.
7. This finding is also evidence that using high-resolution images and better algorithms will improve the performance and accuracy of cancer detection. The remainder of this paper is structured as follows: Section II discusses the published literature along with the Break His dataset.
8. The dataset utilized to obtain experimental results is detailed herein. Section III elaborates on the preprocessing steps and the architecture of the Convolutional Neural Network (CNN) employed in the experiment, followed by neural network classification.
9. Deep learning algorithms necessitate data in the correct format and suitable network parameters tailored to the specific problem. Alternatively, pre-designed networks like AlexNet, MobileNet, Inception, among others, can be employed for streamlined implementation.
10. Indeed, several methods and custom-designed networks have been proposed by scholars for classifying breast cancer, aside from the pre-designed networks mentioned earlier. One such example is Artificial Neural Networks (ANNs), which rely on Maximum Likelihood Estimation (MLE) for classification purposes.

III. PROPOSED METHODOLOGY

This project presents a novel deep learning framework designed for Histopathology image colorization for  diagnosis, integrating principles from machine learning and image classification. Various architectures of Deep Neural Networks are explored, particularly those optimized for processing image data like Convolutional Neural Networks (CNNs). The approach involves utilizing input images labeled as benign or malignant directly from raw pixel data. Through this method, the model learns to discern characteristic visual patterns associated with cancerous tissue, akin to digital staining. These patterns are then utilized by a classifier network to distinguish between non-cancerous and cancerous tissue samples. The CNN is trained using a dataset comprising 2480 benign and 5429 malignant samples represented in the RGB color model.

VI. ACKNOWLEDGMENT

I would like to express my sincere gratitude to all those who contributed to the histopathology image colorization project. Special thanks to [list names or organizations here] for their invaluable expertise, support, and dedication throughout the development process. This project would not have been possible without their collaboration and contributions.

Additionally, I extend my appreciation to the researchers, developers, and collaborators who provided resources, datasets, and feedback that greatly enriched the project.

I am also grateful to [mention any funding sources or sponsors] for their financial support, which enabled the continuation and completion of this work.

Finally, I want to thank the histopathology community for their interest and potential future applications of this project.

[DARAPANENI CHANDRASEKHAR/Team Lead]

Conclusion

In conclusion, the colorization of grayscale histopathological images stands as a transformative advancement, greatly enhancing the interpretability and utility of these critical medical assets. By assigning distinct colors to different tissue components, colorization offers improved tissue differentiation, enhanced contrast, and clearer visualization of subtle morphological details. This augmentation contributes significantly to accurate diagnosis, aids in quantitative analysis, and facilitates educational communication within the field of histopathology. However, it\'s important to acknowledge the limitations inherent in colorization techniques, including subjectivity in color choices, potential generation of artifacts, and resource-intensive computational requirements. Additionally, ethical considerations regarding patient privacy and data security remain paramount. Nonetheless, as research progresses and methodologies evolve, the benefits of colorization continue to outweigh its challenges, underscoring its indispensable role in advancing medical diagnostics, research, and education.

References

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Copyright

Copyright © 2024 CH. Krishna Veni , Darapaneni Chandrasekhar , Bandla Krishnateja , Ganga Nishanth , Elchuri Jaswanth. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.