A Comparative Analysis of Single and Combination Feature Extraction Techniques for Detecting Cervical Cancer Lesions

Kenny, Sean P.; Victor, S. P.

doi:10.21917/ijivp.2016.0170

Cited by 2 publications

(8 citation statements)

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“…They have shown that cellular-level data, namely, cytology, FISH, and electromagnetic spectrum can achieve better outcomes when compared with tissue-level data such as cervicography and colposcopy. S. PradeepKumar Kenny et al [3] represented a comparative study between single and multiple features extraction methodologies. Segmentation applying a multiscale watershed technique is explained for the feature extraction process.…”

Section: Related Workmentioning

confidence: 99%

“…Cancer remains the major leading cause for death among women across the globe [1], [2]. There is a significant prevalence of cervical cancer patients in developing nations such as India and China [3]. As per the World Health Organization (WHO), breast cancer and cervical cancer are more prevalent among women compared to other forms of cancer.…”

Section: Introductionmentioning

confidence: 99%

“…Even though cervical cancer falls into a category of cancers that can be readily prevented through vaccination [2] and has an extended pre-malignant phase [8], [9], [10] which can be utilized for detection and treatment of the disease, lack of awareness is the key reason for this mortality rate in developing countries [11], [9], [12], [13], [14]. Cervical cancer is developed in the cervix area of the vagina the entrance of the uterus in women [3]. HPV vaccination and prevention actions such as regular screening and detection of malignant cells in the cervix help to reduce the mortality ratio occurring due to cervical cancer [8], [15], [9], [16].…”

Section: Introductionmentioning

confidence: 99%

“…Detection and identification of the actual problem, identifying the root cause of the disease, classification between benign and malignant cells within the human body, and suggesting immediate or primary care treatment have become effortless nowadays. Medical image analysis techniques like enhancement, denoising, segmentation, feature extraction, and morphological image processing provide detailed insights into the area of interest due to which doctors and medical teams can proceed with the treatment in no time [23], [3], [5], [6]. IP has a wide spectrum of medical applications viz.…”

Section: Introductionmentioning

confidence: 99%

“…In the last phase, classification of the images into different classes such as superficial cell, intermediate cell, columnar cells, dysplastic cells, carcinoma in situ, etc. to differentiate between normal and abnormal cells are achieved [3], [14].…”

Section: Introductionmentioning

confidence: 99%

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Meticulous Review: Cutting-Edge Cervix Cancer Stratification Using Image Processing And Machine Learning

Bhavsar,

Shrimali

2024

IJCDS

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Cervical cancer has under the top cancer found in women of developing countries since last many years. Classification of cervical cancer through a traditional microscopic approach is a monotonous and prolonged task. Most of the time hospital doctors cannot identify the cancer cells as sometimes the nucleus of a cell, which contains the genetic material (DNA), is typically very small and often not visible to the naked eye. Due to the different perspectives of doctors, cancer stages are classified falsely which leads to low recovery and late medication. The use of Image Processing and Machine Learning technologies can take off misclassification and inaccurate prediction. Although many deep learning techniques are available for cervical cancer cell detection and classification, the performance of such techniques for prediction and classification with real and sample datasets is the main challenge. In this paper, we did a thorough state-of-the-art review of the available current literature. The objective of this paper is to bring forth in-depth knowledge to novice researchers with a thorough understanding of the architecture of the computer-assisted classification process. The current literature is studied, analyzed, and discussed with their approaches, results, and methodologies.

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Section: Related Workmentioning

confidence: 99%