Faculty Publications
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Item Deep Learning for COVID-19(Springer Science and Business Media Deutschland GmbH, 2022) Bs, B.S.; Manoj Kumar, M.V.; Thomas, L.; Ajay Kumar, M.A.; Wu, D.; Annappa, B.; Hebbar, A.; Vishnu Srinivasa Murthy, Y.V.S.Ever since the outbreak in Wuhan, China, a variant of Coronavirus named “COVID 19” has taken human lives in millions all around the world. The detection of the infection is quite tedious since it takes 3–14 days for the symptoms to surface in patients. Early detection of the infection and prohibiting it would limit the spread to only to Local Transmission. Deep learning techniques can be used to gain insights on the early detection of infection on the medical image data such as Computed Tomography (CT images), Magnetic resonance Imaging (MRI images), and X-Ray images collected from the infected patients provided by the Medical institution or from the publicly available databases. The same techniques can be applied to do the analysis of infection rates and do predictions for the coming days. A wide range of open-source pre-trained models that are trained for general classification or segmentation is available for the proposed study. Using these models with the concept of transfer learning, obtained resultant models when applied to the medical image datasets would draw much more insights into the COVID-19 detection and prediction process. Innumerable works have been done by researchers all over the world on the publicly available COVID-19 datasets and were successful in deriving good results. Visualizing the results and presenting the summarized data of prediction in a cleaner, unambiguous way to the doctors would also facilitate the early detection and prevention of COVID-19 Infection. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.Item Infant Brain MRI Segmentation Using Deep Volumetric U-Net with Gamma Transformation(Springer Science and Business Media Deutschland GmbH, 2023) Yeshwanth, G.S.; Annappa, B.; Dodia, S.; Manoj Kumar, M.V.The growth of the brain from infantile to adolescence is very complex and takes a very long period. There are many processes such as myelination, migration, neural induction, and many other time-taking processes to study the development of the brain. This makes it necessary to develop some automatic tools to study the development of the brain. The brain consists mainly of three parts white matter, gray matter, and cerebrospinal fluid. So, quantitative tools will be a great boon for the medical community to deal with the brain if the brain MRI images are segmented into these three different parts. Although there are some tools for segmenting adult MRI images, for 6-month child segmentation, the brain becomes challenging as the white matter and gray matter are almost indistinguishable due to the brain development process. Segmentation of brain MRI images can identify specific patterns that contribute to healthy brain development. The dataset to address this problem had been taken from the Iseg2019 challenge conducted by MICCAI. Segmentation of MRI needs expert doctors. Advancements in computer vision techniques can be used to replace present time-consuming work. This paper proposes a deep learning model for image segmentation using a three-dimensional U-net. The proposed model gives dice values of 93.75, 88.24, and 85.64 for cerebrospinal fluid, gray matter, and white matter. This paper also presents various experimental results of U-net, attention U-net with different modifications. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Classification of Skin Cancer Images using Lightweight Convolutional Neural Network(Institute of Electrical and Electronics Engineers Inc., 2023) Sandeep Kumar, T.; Annappa, B.; Dodia, S.Skin is the most powerful shield human organ that protects the internal organs of the human body from external attacks. This important organ is attacked by a diverse range of microbes such as viruses, fungi, and bacteria causing a lot of damage to the skin. Apart from these microbes, even dust plays important role in damaging skin. Every year several people in the world are suffering from skin diseases. These skin diseases are contagious and spread very fast. There are varieties of skin diseases. Thus it requires a lot of practice to distinguish the skin disease by the doctor and provide treatment. In order to automate this process several deep learning models are used in recent past years. This paper demonstrates an efficient and lightweight modified SqueezeNet deep learning model on the HAM10000 dataset for skin cancer classification. This model has outperformed state-of-the-art models with fewer parameters. As compared to existing deep learning models, this SqueezeNet variant has achieved 99.7%, 97.7%, and 97.04% as train, validation, and test accuracies respectively using only 0.13 million parameters. © 2023 IEEE.Item An Efficient Deep Transfer Learning Approach for Classification of Skin Cancer Images(Springer Science and Business Media Deutschland GmbH, 2023) Naik, P.P.; Annappa, B.; Dodia, S.Prolonged exposure to the sun for an extended period can likely cause skin cancer, which is an abnormal proliferation of skin cells. The early detection of this illness necessitates the classification of der-matoscopic images, making it an enticing study problem. Deep learning is playing a crucial role in efficient dermoscopic analysis. Modified version of MobileNetV2 is proposed for the classification of skin cancer images in seven classes. The proposed deep learning model employs transfer learning and various data augmentation techniques to more accurately classify skin lesions compared to existing models. To improve the per¬formance of the classifier, data augmentation techniques are performed on “HAM10000" (Human Against Machine) dataset to classify seven dif¬ferent kinds of skin cancer. The proposed model obtained a training accuracy of 96.56% and testing accuracy of 93.11%. Also, it has a lower number of parameters in comparison to existing methods. The aim of the study is to aid dermatologists in the clinic to make more accurate diagnoses of skin lesions and in the early detection of skin cancer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Item Optimizing Super-Resolution Generative Adversarial Networks(Springer Science and Business Media Deutschland GmbH, 2023) Jain, V.; Annappa, B.; Dodia, S.Image super-resolution is an ill-posed problem because many possible high-resolution solutions exist for a single low resolution (LR) image. There are traditional methods to solve this problem, they are fast and straightforward, but they fail when the scale factor is high or there is noise in the data. With the development of machine learning algorithms, their application in this field is studied, and they perform better than traditional methods. Many Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs) have been developed for this problem. The Super-Resolution Generative Adversarial Networks (SRGAN) have proved to be significant in this area. Although the SRGAN produces good results with 4 upscaling, it has some shortcomings. This paper proposes an improved version of SRGAN with reduced computational complexity and training time. The proposed model achieved an PPSNR of 29.72 and SSIM value of 0.86. The proposed work outperforms most of the recently developed systems. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.Item Cross-Database Facial Expression Recognition using CNN with Attention Mechanism(Institute of Electrical and Electronics Engineers Inc., 2023) Chandra, J.; Annappa, B.; Rashmi Adyapady, R.Facial expression is one of the most effective and universal ways to express emotions and intentions. It reflects what a person is thinking or experiencing. Thus, the expression recognition is one of the key aspects of understanding non-verbal communication and interpreting emotions in social interactions. Some emotions are very confusing, and separating the features between them becomes difficult because they share the same feature space. For example, the distinction between fear, anger, and disgust is confusing. This work tried to improve the model's class-wise performance to detect each class correctly. A distinct combination of deep-learning models is used to calculate the performance of the model, such as ResNet, XceptionNet, DenseNet, etc. The datasets like Real-world Affective Faces Database (RAF-DB), Japanese Female Facial Expression (JAFFE) & Facial Expression Recognition 2013 Plus (FER+) are used to evaluate the model's performance. The proposed model achieved better results and overcame the previous work's limitations. CDE's performance on RAF-DB and FER+ evaluations was significantly better than the current SOTA methods, with an increase in accuracy of 5.18% and 3.98%, respectively. © 2023 IEEE.Item Abdominal Multi-Organ Segmentation Using Federated Learning(Institute of Electrical and Electronics Engineers Inc., 2024) Yadav, G.; Annappa, B.; Sachin, D.N.Multi-organ segmentation refers to precisely de-lineating and identifying multiple organs or structures within medical images, such as Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI), to outline boundaries and regions for each organ accurately. Medical imaging is crucial to comprehending and diagnosing a wide range of illnesses for which accurate multi-organ image segmentation is often required for successful analysis. Due to the delicate nature of medical data, traditional methods for multi-organ segmentation include centralizing data, which presents serious privacy problems. This centralized training strategy impedes innovation and collaborative efforts in healthcare by raising worries about patient confidentiality, data security, and reg-ulatory compliance. The development of deep learning-based image segmentation algorithms has been hindered by the lack of fully annotated datasets, and this issue is exacerbated in multi-organ segmentation. Federated Learning (FL) addresses privacy concerns in multi-organ segmentation by enabling model training across decentralized institutions without sharing raw data. Our proposed FL-based model for CT scans ensures data privacy while achieving accurate multi-organ segmentation. By leveraging FL techniques, this paper collaboratively trains segmentation models on local datasets held by distinct medical institutions. The expected outcomes encompass achieving high Dice Similarity Coefficient (DSC) metrics and validating the efficacy of the proposed FL approach in attaining precise and accurate segmentation across diverse medical imaging datasets. © 2024 IEEE.Item Semantic Segmentation of Remotely Sensed Images for Land-use and Land-cover Classification: A Comprehensive Review(Taylor and Francis Ltd., 2025) Putty, A.; Annappa, B.; Pariserum Perumal, S.Remotely Sensed Images (RSI) based land-use and land-cover (LULC) mapping facilitates applications such as forest logging, biodiversity protection, and urban topographical kinetics. This process has gained more attention with the widespread availability of geospatial and remote sensing data. With recent advances in machine learning and the possibility of processing nearly real-time information on the computer, LULC mapping methods broadly fall into two categories: (i) framework-dependent algorithms, where mappings are done using the in-built algorithms in Geographical Information System (GIS) software and (ii) framework-independent algorithms, which are mainly based on deep learning techniques. Both approaches have their unique advantages and challenges. Along with the working patterns and performances of these two methodologies, this comprehensive review thoroughly analyzes deep learning architectures catering different technical capabilities like feature extraction, boundary extraction, transformer-based mechanism based mechanism, attention mechanism, pyramid pooling and lightweight models. To fine-tune these semantic segmentation processes, current technical and domain challenges and insights into future directions for analysing RSIs of varying spatial and temporal resolutions are summarized. Cross domain users with application specific requirements can make use of this study to select appropriate LULC semantic segmentation models. © 2025 IETE.Item COVID-19: Automatic detection from X-ray images by utilizing deep learning methods(Elsevier Ltd, 2021) Nigam, B.; Nigam, A.; Jain, R.; Dodia, S.; Arora, N.; Annappa, B.In recent months, a novel virus named Coronavirus has emerged to become a pandemic. The virus is spreading not only humans, but it is also affecting animals. First ever case of Coronavirus was registered in city of Wuhan, Hubei province of China on 31st of December in 2019. Coronavirus infected patients display very similar symptoms like pneumonia, and it attacks the respiratory organs of the body, causing difficulty in breathing. The disease is diagnosed using a Real-Time Reverse Transcriptase Polymerase Chain reaction (RT-PCR) kit and requires time in the laboratory to confirm the presence of the virus. Due to insufficient availability of the kits, the suspected patients cannot be treated in time, which in turn increases the chance of spreading the disease. To overcome this solution, radiologists observed the changes appearing in the radiological images such as X-ray and CT scans. Using deep learning algorithms, the suspected patients’ X-ray or Computed Tomography (CT) scan can differentiate between the healthy person and the patient affected by Coronavirus. In this paper, popular deep learning architectures are used to develop a Coronavirus diagnostic systems. The architectures used in this paper are VGG16, DenseNet121, Xception, NASNet, and EfficientNet. Multiclass classification is performed in this paper. The classes considered are COVID-19 positive patients, normal patients, and other class. In other class, chest X-ray images of pneumonia, influenza, and other illnesses related to the chest region are included. The accuracies obtained for VGG16, DenseNet121, Xception, NASNet, and EfficientNet are 79.01%, 89.96%, 88.03%, 85.03% and 93.48% respectively. The need for deep learning with radiologic images is necessary for this critical condition as this will provide a second opinion to the radiologists fast and accurately. These deep learning Coronavirus detection systems can also be useful in the regions where expert physicians and well-equipped clinics are not easily accessible. © 2021 Elsevier LtdItem A novel receptive field-regularized V-net and nodule classification network for lung nodule detection(John Wiley and Sons Inc, 2022) Dodia, S.; Annappa, B.; Mahesh, M.Recent advancements in deep learning have achieved great success in building a reliable computer-aided diagnosis (CAD) system. In this work, a novel deep-learning architecture, named receptive field regularized V-net (RFR V-Net), is proposed for detecting lung cancer nodules with reduced false positives (FP). The method uses a receptive regularization on the encoder block's convolution and deconvolution layer of the decoder block in the V-Net model. Further, nodule classification is performed using a new combination of SqueezeNet and ResNet, named nodule classification network (NCNet). Postprocessing image enhancement is performed on the 2D slice by increasing the image's intensity by adding pseudo-color or fluorescence contrast. The proposed RFR V-Net resulted in dice similarity coefficient of 95.01% and intersection over union of 0.83, respectively. The proposed NCNet achieved the sensitivity of 98.38% and FPs/Scan of 2.3 for 3D representations. The proposed NCNet resulted in considerable improvements over existing CAD systems. © 2021 Wiley Periodicals LLC.