Multifractal Texture Analysis of Salivary Fern Pattern for Oral Pre-Cancers and Cancer Assessment

Sharma, Neha; Nawn, Debaleena; Pratiher, Sawon; Shome, Sayani; Chatterjee, Rahul; Biswas, Karabi; Pal, Mousumi; Paul, Ranjan Rashmi; Dutta, Srimonti; Chatterjee, Jyotirmoy

doi:10.1109/jsen.2021.3053262

Cited by 15 publications

(10 citation statements)

References 77 publications

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“…On the contrary, the dynamical behaviour of a multifractal system is characterized by a continuous spectrum of exponents [40]. Besides, multifractal geometry is ubiquitously abundant in nature [37], as found in diverse areas ranging from molecular interactions [38], finance [41], language [42], earthquake's distribution [43], electroencephalogram [44], [45], uterine electromyography [46], medical imaging [47], cancerous tissues [48]- [50], retinal vessels [51], brain tumor [52], and so forth.…”

Section: Research Motivation and Research Questionsmentioning

confidence: 99%

Multifractality in Parasympathetic-Sympathetic Responses and their Interactions: From 1/f Noise to Long-range Correlations

Pratiher¹,

Sahoo²,

Lavanuru³

et al. 2023

Preprint

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Section: Research Motivation and Research Questionsmentioning

confidence: 99%

Multifractality in Parasympathetic-Sympathetic Responses and their Interactions: From 1/f Noise to Long-range Correlations

Pratiher¹,

Sahoo²,

Lavanuru³

et al. 2023

Preprint

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“…The classification accuracy reached 97% using the SVM classifier. Several researchers used multifractal in medical image analysis [50][51][52] .…”

Section: Very Severe Cogniɵve Decline (Very Severe Demenɵa)mentioning

confidence: 99%

A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm

Elgammal

Zahran

Abdelsalam

2022

Sci Rep

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Alzheimer's Disease (AD) is considered one of the most diseases that much prevalent among elderly people all over the world. AD is an incurable neurodegenerative disease affecting cognitive functions and were characterized by progressive and collective functions deteriorating. Remarkably, early detection of AD is essential for the development of new and invented treatment strategies. As Dementia causes irreversible damage to the brain neurons and leads to changes in its structure that can be described adequately within the framework of multifractals. Hence, the present work focus on developing a promising and efficient computing technique to pre-process and classify the AD disease especially in the early stages using multifractal geometry to extract the most changeable features due to AD. Then, A machine learning classification algorithm (K-Nearest Neighbor) has been implemented in order to classify and detect the main four early stages of AD. Two datasets have been used to ensure the validation of the proposed methodology. The proposed technique has achieved 99.4% accuracy and 100% sensitivity. The comparative results show that the proposed classification technique outperforms is recent techniques in terms of performance measures.

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“…10,11 Multifractal analysis has emerged as a powerful tool for examining the intricate characteristics of medical signals. [12][13][14][15][16][17] By assessing the multifractal properties of medical signals, researchers gain insights into the complex interplay of diverse scales and singularities within the signals. This study employs MFDFA to showcase its potential in the non-invasive early diagnosis of cervical cancer through colposcopy images.…”

Section: Introductionmentioning

confidence: 99%

Multifractal analysis for detection of different stages in cervical cancer from colposcopy images

Singha Deo,

Pal,

Panigrahi

et al. 2024

Optical Biopsy XXII: Toward Real-Time Spectroscopic Imaging and Diagnosis

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Cervical cancer is of significant health concern globally, particularly in developing countries where access to advanced healthcare facilities and medical resources is limited, leading to increased mortality rates. The gold standard for diagnosing CIN (cervical intraepithelial neoplasia) and invasive cervical cancer involves performing a colposcopy-guided biopsy followed by a pathological diagnosis. However, its effectiveness is challenged by limited sensitivity in distinguishing between various stages of cervical cancer, especially in regions where there is a shortage of skilled colposcopists and insufficient access to medical resources. This study presents a method for categorizing infectious, pre-cancerous, and cancerous conditions through the application of multifractal analysis, specifically two dimensional multifractal detrended fluctuation analysis (2D MFDFA), using images obtained through colposcopy. The utilization of multifractal parameters, namely the generalized Hurst exponent and the width of the singularity spectrum, in the analysis distinctly demonstrated variations among the infectious, precancerous, and cancerous conditions. Therefore, it offers valuable insights to healthcare professionals, assisting in the accurate classification and effective management of cervical cancer using Hurst exponent and multifractal spectrum width.

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Multifractal Texture Analysis of Salivary Fern Pattern for Oral Pre-Cancers and Cancer Assessment

Cited by 15 publications

References 77 publications

Multifractality in Parasympathetic-Sympathetic Responses and their Interactions: From 1/f Noise to Long-range Correlations

Multifractality in Parasympathetic-Sympathetic Responses and their Interactions: From 1/f Noise to Long-range Correlations

A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm

Multifractal analysis for detection of different stages in cervical cancer from colposcopy images

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