Investigator

Mayukha Pal

R&D Principal Program Manager · ABB Ltd, ABB Ability Innovation Center

Research Interests

MPMayukha Pal
Papers(4)
Empirical Mode Decomp…Wavelet scattering tr…A smartphone‐based st…Cervical <scp>pre‐can…
Collaborators(3)
Asima PradhanPrasanta K PanigrahiShivam Shukla
Institutions(3)
Abb IndiaIndian Institute Of T…Indian Institute of S…

Papers

Empirical Mode Decomposition and Grassmann Manifold‐Based Cervical Cancer Detection

ABSTRACTCervical cancer is a prevalent malignancy affecting the female reproductive system and is recognized as a prominent factor to female mortality on a global scale. Timely and precise detection of various stages of cervical cancer plays a crucial role in enhancing the chances of successful treatment and extending patient survival. Fluorescence spectroscopy stands out as a highly sensitive method for identifying biochemical alterations associated with cancer and numerous other pathological conditions. In our study, empirical mode decomposition (EMD) and Grassmann manifold (GM) learning are explored for reliable cancer detection using fluorescence spectral signals collected from 110 subjects representing various categories of the human cervix. Initially, EMD is used to decompose the signal into several multi‐feature intrinsic mode functions (IMFs) on a spectral scale. Each IMF demonstrates uniqueness by capturing the inherent frequency characteristics within the signal, thus facilitating the extraction of signal features. The GM representation of IMFs is employed for investigating the non‐linear subspace structure within spectral signals, which is subsequently followed by a low‐rank representation to transform and analyze the spectral signals. The GM allows for the extraction of relevant information, reduction of dimensionality, and exploration of complex relationships within data, ultimately contributing to improved diagnosis. Mutual information is further used for feature selection to reduce the number of features and hence the computational cost. When the selected features were employed for classification, the Random Forest (RF) classifier attained a high five‐fold validation accuracy of 99% and exhibited a minimal standard deviation of 0.02. Other state‐of‐the‐art machine learning classifiers were also used and compared with the RF model.

Wavelet scattering transform and entropy features in fluorescence spectral signal analysis for cervical cancer diagnosis

Abstract Cervical cancer is a prevalent malignant tumor within the female reproductive system and is regarded as a prominent cause of female mortality on a global scale. Timely and precise detection of various phases of cervical cancer holds the potential to substantially enhance both the rate of successful treatment and the duration of patient survival. Fluorescence spectroscopy is a highly sensitive method for detecting the biochemical changes that arise during cancer progression. In our study, fluorescence spectral data is collected from a diverse group of 110 subjects. The potential of the scattering transform technique for the purpose of cancer detection is explored. The processed signal undergoes an initial decomposition into scattering coefficients using the wavelet scattering transform (WST). Subsequently, the scattering coefficients are subjected to computation for fuzzy entropy, dispersion entropy, phase entropy, and spectral entropy, for effectively characterizing the fluorescence spectral signals. These combined features generated through the proposed approach are then fed to 1D convolutional neural network (CNN) classifier to classify them into normal, pre-cancerous, and cancerous categories, thereby evaluating the effectiveness of the proposed methodology. We obtained mean classification accuracy of 97% using 5-fold cross-validation. This demonstrates the potential of combining WST and entropic features for analyzing fluorescence spectroscopy signals using 1D CNN classifier that enables early cancer detection in contrast to prevailing diagnostic methods.

Cervical pre‐cancer classification using entropic features and CNN: In vivo validation with a handheld fluorescence probe

AbstractCervical cancer is one of the most prevalent forms of cancer, with a lengthy latent period and a gradual onset phase. Conventional techniques are found to be severely lacking in real time detection of disease progression which can greatly enhance the cure rate. Due to their high sensitivity and specificity, optical techniques are emerging as reliable tools, particularly in case of cancer. It has been seen that biochemical changes are better highlighted through intrinsic fluorescence devoid of interference from absorption and scattering. Its effectiveness in in‐vivo conditions is affected by the fact that the intrinsic spectral signatures vary from patient to patient, as well as in different population groups. Here, we overcome this limitation by collectively enumerating the subtle changes in the spectral profiles and correlations through an information theory based entropic approach, which significantly amplifies the minute spectral variations. In conjunction with artificial intelligence (AI)/machine learning (ML) tools, it yields high specificity and sensitivity with a small dataset from patients in clinical conditions, without artificial augmentation. We have used an in‐house developed handheld probe (i‐HHP) for extracting intrinsic fluorescence spectra of human cervix from 110 different subjects drawn from diverse population groups. The average classification accuracy of the proposed methodology using 10‐fold cross validation is 93.17%. A combination of polarised fluorescence spectra from i‐HHP and the proposed classifier is proven to be minimally invasive with the ability to diagnose patients in real time. This paves the way for effective use of relatively smaller sized sensitive fluorescence data with advanced AI/ML tools for early cervical cancer detection in clinics.

73Works
4Papers
3Collaborators
Uterine Cervical NeoplasmsPrecancerous Conditions

Positions

2019–

R&D Principal Program Manager

ABB Ltd · ABB Ability Innovation Center

2006–

Engineering Technical Leader

General Electric (India) · India Innovation Center

2003–

Research Engineer

Optiwave Photonics Ltd · R&D

Education

2016

Ph.D.

CR Rao Advanced Institute of Mathematics, Statistics and Computer Science

2003

Masters in Electronics

University of Hyderabad · CASEST

2000

Bachelors

Berhampur University

1997

Intermediate

CHSE

1995

High School

BSE

Country

IN

Keywords
Cognitive Neuroscience;Biomedical Signal processing;Econophysics;Circuit Breaker Analytics;