Dr. Deepthy Mary Alex

@mangalam.ac.in

Associate Professor in Department of Electronics and Communication Engineeing
Mangalam College of Engineering

Dr. Deepthy Mary Alex


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https://researchid.co/deepthymalex

RESEARCH, TEACHING, or OTHER INTERESTS

Engineering, Computer Vision and Pattern Recognition, Artificial Intelligence, Signal Processing
14

Scopus Publications

Scopus Publications

  • Assistance system for the visually challenged
    Deepthy Mary Alex, Meera Panicker Pulupra Ravikumar, Afssy Basheer, Anjitha Anil, Akash Puthenparambil Santhosh, Tenil Sojan
    Aip Conference Proceedings, 2025
  • A hybrid model of random forest ensemble and resample for cardiotocography data classification
    R. HEPHZIBAH
    Sigma Journal of Engineering and Natural Sciences, 2025
    Fetal health monitoring is essential as it leads to increased mortality rates in fetuses.Cardiotocography is a medical technique used by obstetricians to monitor fetal health during labor, particularly in cases involving complications.Though various works have been carried out in the classification of CTG data there seems to be a need for improvement in achieving significant accuracy levels.In this work, first, we implemented the Smote Tomek sampling technique to create a balanced dataset.Then, the balanced data is employed for classification in the Random Forest ensemble with a bagging classifier.Our technique's performance is assessed using metrics including accuracy, precision, recall, and F1-score.Experimental findings reveal our method achieves an accuracy of 98.5%, outperforming not only other classifiers examined in the study but also surpassing deep learning algorithms.Hence, the findings of our study highlight the effectiveness of our approach in classifying Cardiotocography data, suggesting the potential for enhancing fetal health monitoring during labor and for improved obstetric care.
  • Design of a Circular Loop Antenna for 5G Millimeter-Wave Application Using Genetic Optimization Algorithm
    Muddineni Raveendra, Sreelakshmy R, Dhanya G, Sruthy R, Deepthy Mary Alex, Saam Prasanth Dheeraj Pedapalli
    4th Wireless Antenna and Microwave Symposium Wams 2025, 2025
    This paper presents the design of a circular loop antenna intended for millimeter-wave applications within the frequency range of 26 GHz to 30 GHz. The objective is to enhance the antenna's bandwidth, which is achieved through the use of a genetic algorithm. The genetic algorithm employs tournament selection to identify the most suitable parents from the population. Additionally, a multi-objective function is formulated to optimize both the bandwidth and gain of the proposed antenna. The antenna design utilizes Rogers-4003 dielectric material, characterized by a dielectric constant (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\varepsilon_{r}$</tex>) of 3.55 and a loss tangent (<tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\delta$</tex>) of 0.0027. After successfully optimizing the proposed antenna, its performance is evaluated by analyzing the return loss and gain characteristics. Finally, after meeting the predefined objectives, an analysis of the radiation characteristics is conducted to gain insights into the antenna's radiation properties in the E-plane and H-plane.
  • DANNET: deep attention neural network for efficient ear identification in biometrics
    Deepthy Mary Alex, Kalpana Chowdary M., Hanan Abdullah Mengash, Venkata Dasu M., Natalia Kryvinska, Chinna Babu J., Ajmeera Kiran
    Peerj Computer Science, 2024
    Biometric identification, particularly ear biometrics, has gained prominence amidst the global prevalence of mask-wearing, exacerbated by the COVID-19 outbreak. This shift has highlighted the need for reliable biometric systems that can function effectively even when facial features are partially obscured. Despite numerous proposed convolutional neural network (CNN) based deep learning techniques for ear detection, achieving the expected efficiency and accuracy remains a challenge. In this manuscript, we propose a sophisticated method for ear biometric identification, named the encoder-decoder deep learning ensemble technique incorporating attention blocks. This innovative approach leverages the strengths of encoder-decoder architectures and attention mechanisms to enhance the precision and reliability of ear detection and segmentation. Specifically, our method employs an ensemble of two YSegNets, which significantly improves the performance over a single YSegNet. The use of an ensemble method is crucial in ear biometrics due to the variability and complexity of ear shapes and the potential for partial occlusions. By combining the outputs of two YSegNets, our approach can capture a wider range of features and reduce the risk of false positives and negatives, leading to more robust and accurate segmentation results. Experimental validation of the proposed method was conducted using a combination of data from the EarVN1.0, AMI, and Human Face datasets. The results demonstrate the effectiveness of our approach, achieving a segmentation framework accuracy of 98.93%. This high level of accuracy underscores the potential of our method for practical applications in biometric identification. The proposed innovative method demonstrates significant potential for individual recognition, particularly in scenarios involving large gatherings. When complemented by an effective surveillance system, our method can contribute to improved security and identification processes in public spaces. This research not only advances the field of ear biometrics but also provides a viable solution for biometric identification in the context of mask-wearing and other facial obstructions.
  • Retinal Image Enhancement based on illumination component and gamma correction
    S. P. James, D. A. Chandy, D. M. Alex
    Iet Conference Proceedings, 2023
    Majority of the human eye diseases like Diabetes Retinopathy, Glaucoma and Age related macular Edema(AMD) can be diagnosed by analysing the retinal images. But the non-uniform illumination, low contrast and noise in the images causes difficulty in the diagnosis. In the proposed method the calculated illumination component of the image is subjected to Gamma correction to suppress un-even illumination. The proposed method performed better compared to similar algorithms in terms of various performance indicators and also gave sharper images without affecting its natural appearance.
  • YSegNet: a novel deep learning network for kidney segmentation in 2D ultrasound images
    Deepthy Mary Alex, D. Abraham Chandy, A. Hepzibah Christinal, Arvinder Singh, M. Pushkaran
    Neural Computing and Applications, 2022
  • A Hybrid Random Forest Classifier for Chronic Kidney Disease Prediction from 2D Ultrasound Kidney Images
    Deepthy Mary Alex, D. Abraham Chandy, A. Hepzibah Christinal, Arvinder Singh, M. Pushkaran
    International Journal of Pattern Recognition and Artificial Intelligence, 2022
    Chronic kidney disease (CKD) is one of the causes of mortality in almost all countries across the globe and the notable thing is its asymptomatic nature in the early stages. This disease is characterized by the gradual loss of kidney function in an individual. Frequently chronic kidney disease is diagnosed based on the Estimated Glomerular Filtration Rate (eGFR) determined from blood and urine tests. In order to reduce the risk factors arising due to chronic kidney disease, it is essential to be diagnosed in the earlier stages itself. This work proposes an automated chronic kidney disease detection based on the textural features of the kidney using a hybrid random forest classifier from 2D ultrasound kidney images. The proposed classifier is compared with the other competing machine learning classifiers through experimenting on a dataset of 150 images and gives a better accuracy of [Formula: see text] with [Formula: see text] of recall and precision, thus proving it to be promising in detecting CKD noninvasively in the early stages.
  • Exploration of a framework for the identification of chronic kidney disease based on 2d ultrasound images: A survey
    Deepthy Mary Alex, D. Abraham Chandy
    Current Medical Imaging, 2021
    Background: Chronic kidney disease (CKD) is a fatal disease that ultimately results in kidney failure. The primary threat is the aetiology of CKD. Over the years, researchers have proposed various techniques and methods to detect and diagnose the disease. The conventional method of detecting CKD is the determination of the estimated glomerular filtration rate by measuring creatinine levels in blood or urine. Conventional methods for the detection and classification of CKD are tedious; therefore, several researchers have suggested various alternative methods. Recently, the research community has shown keen interest in developing methods for the early detection of this disease using imaging modalities such as ultrasound, magnetic resonance imaging, and computed tomography. Discussion: The study aimed to conduct a systematic review of various existing techniques for the detection and classification of different stages of CKD using 2D ultrasound imaging of the kidney. The review was confined to 2D ultrasound images alone, considering the feasibility of implementation even in underdeveloped countries because 2D ultrasound scans are more cost effective than other modalities. The techniques and experimentation in each work were thoroughly studied and discussed in this review. Conclusion: This review displayed the cutting-age research, challenges, and possibilities of further research and development in the detection and classification of CKD.
  • Evaluation of Inpainting in Speckled and Despeckled 2D Ultrasound Medical Images
    Deepthy Mary Alex, D. Abraham Chandy
    Proceedings 2020 Advanced Computing and Communication Technologies for High Performance Applications Accthpa 2020, 2020
    Image processing has paved its way through various applications in the medical field, where researches are being carried out to prompt and improvise the care and treatment given to patients in all aspects. Medical image processing applications concerns the need for obtaining images as per the requirement. Inpainting of markers on medical images generated using various modalities is crucial to process the images for numerous applications. One of the inherent and profoundly seen problem in medical images, especially in the ultrasound images is the speckle noise. It alters the edges, fine details and other features of organs that is required for interpretation. In this paper, the need for despeckling of 2D ultrasound images is studied by evaluating the performance metrics for inpainting based on fast marching algorithm of speckled and despeckled 2D ultrasound images.
  • Speckle noise suppression in 2D ultrasound kidney images using local pattern based topological derivative
    Deepthy Mary Alex, A. Hepzibah Christinal, D. Abraham Chandy, Arvinder Singh, M Pushkaran
    Pattern Recognition Letters, 2020
  • Morphology based inpainting for removal of markers in medical images
    International Journal of Scientific and Technology Research, 2020
  • Investigations on performances of pre-trained u-net models for 2d ultrasound kidney image segmentation
    Deepthy Mary Alex, D. Abraham Chandy
    Lecture Notes of the Institute for Computer Sciences Social Informatics and Telecommunications Engineering Lnicst, 2020
  • Comparison of PSO and Sequential Search Algorithms for Improvisation of Entropy-Based Ear Localization
    D. Abraham Chandy, Hepzibah A. Christinal, Rakhi Chandrasekaran, Deepthy Mary Alex
    Advances in Intelligent Systems and Computing, 2020
  • Rationalizing of Morphological Renal Parameters and eGFR for Chronic Kidney Disease Detection
    Deepthy Mary Alex, D. Abraham Chandy, Anand Paul
    Advances in Computerized Analysis in Clinical and Medical Imaging, 2019