SVM classifier of cervical histopathology images based on texture and morphological features

He, Siqi; Xiao, Bo; Wei, Huajiang; Huang, Shenjiao; Chen, Tongsheng

doi:10.3233/thc-220031

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…Table 2 includes all the factors(1-19) considered by different works for prediction of cervical cancer. Recently, AI has been increasingly utilised to identify a wide range of illnesses [29], [30], [31], [32]. The use of AI in cervical cancer screening has improved diagnostic accuracy and helped alleviate the problem of an insufficient workforce [33], [34].…”

Section: Literature Surveymentioning

confidence: 99%

“…Nevertheless, the enhanced accuracy, specificity, sensitivity, range from 94.17% to 94.69%, 93.37% to 94.72%, and 93% to 95.17%, respectively, using an integrated strategy. For Hepatocellular Carcinoma (HCC) prediction, hybrid models including Random Forest, Ridge Regression, LASSO Regression, Genetic Algorithm Optimization, and three machine learning classifiers are suggested in [29]. The suggested approach involves feature selection/optimization, classification, and data pre-processing.…”

Section: Continued On Next Pagementioning

confidence: 99%

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A Cervical Cancer Detection System Using Genetic Algorithm Enhanced Context-Aware Feature Selection

Iqbal,

Qureshi,

Chishti

2024

Preprint

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Cervical cancer is the fourth highest cause of death among women, but in its early stages, it shows no symptoms. A lack of effective early diagnosis is the fundamental cause of the disease's prevalence and poses the greatest obstacle to researchers. Pap smears, human papillomavirus tests, colposcopy, and biopsies are all invasive procedures that necessitate the assistance of medical professionals; they are time-consuming and do not identify cancer until a later stage. Thus, the absence of early symptoms and lack of invasive diagnostics, highlight the need for utilizing machine learning and deep learning approaches to revolutionize cervical cancer detection. To address this, we propose GCFS-CC(Genetic Algorithm based Context-aware Feature Selection for Cervical Cancer), which is a framework to detect cervical cancer from Pap smear images of cervical cells and classify these using a deep learning method. In our work, we use a genetic algorithm which is a bio-inspired algorithm, to select the best features from the acquired images. For feature selection, we incorporate the context-aware entropy gain method. We evaluated our model on one data set which combined a total of 917 normal and cancerous images, divided into seven classes. Using the selected features we build a deep learning model to validate our proposed framework. The results showed that after feature selection, the performance metrics(accuracy, precision, and recall), all improved. These results pave the way for GCFS-CC to be a valuable tool for early cervical cancer detection. This, however, comes with certain challenges, availability of high-quality images and proper parameter tuning for the genetic algorithm required for feature selection. To successfully implement machine learning-based cervical cancer detection into clinical practice, it will be necessary to address these challenges. Furthermore, any ethical and privacy concerns associated with the use of machine learning algorithms in healthcare settings must be addressed.

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Section: Literature Surveymentioning

confidence: 99%

Section: Continued On Next Pagementioning

confidence: 99%

A Cervical Cancer Detection System Using Genetic Algorithm Enhanced Context-Aware Feature Selection

Iqbal,

Qureshi,

Chishti

2024

Preprint

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“…Focused medicines can be created using this knowledge for specific subgroups of breast cancer. SVMs can also be used to analyse medical imagery, where they might find features or abnormalities that could indicate disease (Bhattacharjee et al, 2019; He et al, 2022). SVMs can be used, for instance, to spot regions of interest in MRI scans or to find malignant tumours in mammograms.…”

Section: Applications Of Deep and Machine Learning In Medical Fieldsmentioning

confidence: 99%

Advancements in medical diagnosis and treatment through machine learning: A review

Ahsan,

Khan,

Khan

et al. 2023

Expert Systems

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The aptness of machine learning (ML) to learn from large datasets, discover trends, and make predictions has demonstrated its potential to metamorphose the medical field. Medical data analysis with ML algorithms can improve patient outcomes in terms of both treatment and diagnosis. This paper investigates the numerous possibilities of ML in the medical industries, including radiology, pathology, genomics, and clinical decision‐making. It also goes over the benefits and drawbacks of ML in various sectors as well as the limitations that come with its application. It illustrates the potential advantages of ML, such as better accuracy and efficiency in diagnosis and individualized treatment programs, through a review of previous studies. Lastly, it provides perspectives on prospective advancements and prospects for the discipline. This study also intends to investigate the applications of deep learning (DL) in the medical field. DL algorithms have performed exceptionally satisfactorily in several healthcare‐related fields. The main conclusions of the study are summarized, and their ramifications for the healthcare sector are discussed in this paper's conclusion. This paper intends to contribute to a greater understanding of the prevailing state of the discipline and the possibility for future developments by emphasizing the prospects of these methodologies to alter medical study and clinical practice.

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“…However, the application of machine learning in previous studies mainly focuses on radiological images. Recent studies applied machine learning to histopathological images analysis including breast cancer classification [23][24][25] and cervical cancer detection [26].…”

Section: Machine Learning In Cancer Diagnosismentioning

confidence: 99%

AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis

Cheung,

Wu,

et al. 2023

Cancers

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Background: Glioblastoma (GBM) is one of the most common malignant primary brain tumors, which accounts for 60–70% of all gliomas. Conventional diagnosis and the decision of post-operation treatment plan for glioblastoma is mainly based on the feature-based qualitative analysis of hematoxylin and eosin-stained (H&E) histopathological slides by both an experienced medical technologist and a pathologist. The recent development of digital whole slide scanners makes AI-based histopathological image analysis feasible and helps to diagnose cancer by accurately counting cell types and/or quantitative analysis. However, the technology available for digital slide image analysis is still very limited. This study aimed to build an image feature-based computer model using histopathology whole slide images to differentiate patients with glioblastoma (GBM) from healthy control (HC). Method: Two independent cohorts of patients were used. The first cohort was composed of 262 GBM patients of the Cancer Genome Atlas Glioblastoma Multiform Collection (TCGA-GBM) dataset from the cancer imaging archive (TCIA) database. The second cohort was composed of 60 GBM patients collected from a local hospital. Also, a group of 60 participants with no known brain disease were collected. All the H&E slides were collected. Thirty-three image features (22 GLCM and 11 GLRLM) were retrieved from the tumor volume delineated by medical technologist on H&E slides. Five machine-learning algorithms including decision-tree (DT), extreme-boost (EB), support vector machine (SVM), random forest (RF), and linear model (LM) were used to build five models using the image features extracted from the first cohort of patients. Models built were deployed using the selected key image features for GBM diagnosis from the second cohort (local patients) as model testing, to identify and verify key image features for GBM diagnosis. Results: All five machine learning algorithms demonstrated excellent performance in GBM diagnosis and achieved an overall accuracy of 100% in the training and validation stage. A total of 12 GLCM and 3 GLRLM image features were identified and they showed a significant difference between the normal and the GBM image. However, only the SVM model maintained its excellent performance in the deployment of the models using the independent local cohort, with an accuracy of 93.5%, sensitivity of 86.95%, and specificity of 99.73%. Conclusion: In this study, we have identified 12 GLCM and 3 GLRLM image features which can aid the GBM diagnosis. Among the five models built, the SVM model proposed in this study demonstrated excellent accuracy with very good sensitivity and specificity. It could potentially be used for GBM diagnosis and future clinical application.

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SVM classifier of cervical histopathology images based on texture and morphological features

Cited by 5 publications

References 27 publications

A Cervical Cancer Detection System Using Genetic Algorithm Enhanced Context-Aware Feature Selection

A Cervical Cancer Detection System Using Genetic Algorithm Enhanced Context-Aware Feature Selection

Advancements in medical diagnosis and treatment through machine learning: A review

AI Deployment on GBM Diagnosis: A Novel Approach to Analyze Histopathological Images Using Image Feature-Based Analysis

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