Sreedhar Kollem
@sru.edu.in
SR University
EDUCATION
B. Tech, M. Tech, Ph.D.
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Sreedhar Kollem, Chandrasekhar Sirigiri, and Samineni Peddakrishna
Elsevier BV
M. Naresh, V. Siva Nagaraju, Sreedhar Kollem, Jayendra Kumar, and Samineni Peddakrishna
Elsevier BV
Ch. Rajendra Prasad, Pillalamarri Shivapriya, Naragani Bhargavi, Nagaraj Ravula, Supraja Lakshmi Devi Sripathi, and Sreedhar Kollem
AIP Publishing
Srinivas Samala, Udutha Sahithi, Avunoori Bharath Kumar, Odela Sravan Kumar, Veladandi Ramya Sri, Ch. Rajendra Prasad, and Sreedhar Kollem
IEEE
The agricultural industry is increasingly adopting Deep Learning methodologies to tackle obstacles related to weed identification and categorization, with the ultimate goal of enhancing crop productivity. However, the complexity stems from the striking similarity in colours, forms, and textures between weeds and crops, specifically when they are in the process of growing. Automated and precise weed identification is of the utmost importance to minimize agricultural losses and maximize the use of resources. The analysis of the literature under review enhances comprehension of the obstacles, remedies, and prospects associated with weed identification and categorization via CNN models. To address these obstacles, we have devised a solution that entails the construction and refinement of a customized Convolutional Neural Network model. The experiment employs the Four-class weed dataset obtained from Kaggle and utilizes the Adaptive Moment Estimation optimizer during the training process. The accuracy of 96.58% is demonstrated by the proposed model in accurately identifying and categorizing weeds in the fields.
Sreedhar Kollem, Pati Harika, Janagam Vignesh, Peddoju Sairam, Adunuthula Ramakanth, Samineni Peddakrishna, Srinivas Samala, and Ch. Rajendra Prasad
IEEE
The multimodal MRI scans described in this article are used to categorize brain tumors based on their location and size. Brain tumors need to be categorized in order to assess the tumors and choose the appropriate course of treatment for each class. Many different imaging methods are used to detect brain tumors. However, because MRI does not use ionizing radiation and generates better images, it is commonly used. Using deep learning (DL), a branch of machine learning has recently demonstrated impressive results, particularly in segmentation and classifiable tasks. This paper proposes a convolutional neural network-based deep learning model (DL) that uses transfer learning and EfficientNet to classify various kinds of brain cancers using publically accessible datasets. The first divides cancers into three categories: glioma, meningioma, and pituitary tumor. Compared to conventional deep learning techniques, the suggested approach produces superior results. The Python platform can be used to complete the task.
Srinivas Samala, Aakash Sreeram, Lakshmi Sree Vindhya Sarva, Sreedhar Kollem, Kedhareshwar Rao Vanamala, and Chandrashekar Valishetti
IEEE
Due to its aggressiveness and the difficulties in detecting it in time, lung cancer is a leading cause of cancer-related deaths. Unfortunately, it is often detected at an advanced stage. Although it is a significant difficulty, early detection is essential for individual survival. Radiographs of the chest and computed tomography scans are the first lines of diagnostics. On the other hand, incorrect diagnoses could result from the possibility of benign nodules. Early on, it is especially difficult to differentiate benign nodules from malignant ones due to their extremely comparable characteristics. To address this problem, a novel AdaBoost-SVM model is suggested to improve the accuracy of malignant nodule diagnosis. Kaggle is the source of the dataset that is used to train the model. The proposed model exhibits a remarkable accuracy rate of 97.96%, surpassing the performance of conventional SVM methods. This development imparts the potential for enhanced precision and dependability in the crucial initial phases of lung cancer diagnosis
Sreedhar Kollem, Kodari Poojitha, Naroju Brahma Chary, Pulluri Saicharan, Kampelly Anvesh, Samineni Peddakrishna, and Ch. Rajendra Prasad
IEEE
The viability of agriculture and the security of the world's food supply are seriously threatened by plant diseases. Detecting these diseases promptly and accurately is crucial for effective disease control and minimizing crop output losses. Deep learning algorithms have shown possibilities recently as a method for accurately and automatically classifying plant diseases. This research presents an innovative deep-learning framework designed for plant disease classification, incorporating transfer learning and customized convolutional neural networks (CNNs). The proposed framework comprises three main phases: data pre-processing or transfer learning, feature extraction, and disease classification. This article presents a new approach to plant disease categorization using deep learning. It combines convolutional neural networks (CNNs) with transfer learning. Through this method, plant diseases can be identified with precision and automation across diverse plant species and types of disease. This facilitates more effective disease management, safeguarding the security of the global food supply. Comparative analysis indicates that the proposed method outperforms traditional approaches, yielding superior results.
Sreedhar Kollem, Ch. Rajendra Prasad, J. Ajayan, Sreejith S., LMI Leo Joseph, and Patteti Krishna
Bentham Science Publishers Ltd.
Background: In image processing, image segmentation is a more challenging task due to different shapes, locations, image intensities, etc. Brain tumors are one of the most common diseases in the world. So, the detection and segmentation of brain tumors are important in the medical field. Objective: The primary goal of this work is to use the proposed methodology to segment brain MRI images into tumor and non-tumor segments or pixels. Methods: In this work, we first selected the MRI medical images from the BraTS2020 database and transferred them to the contrast enhancement phase. Then, we applied thresholding for contrast enhancement to enhance the visibility of structures like blood arteries, tumors, or abnormalities. After the contrast enhancement process, the images were transformed into the image denoising phase. In this phase, a fourth-order partial differential equation was used for image denoising. After the image denoising process, these images were passed on to the segmentation phase. In this segmentation phase, we used an elephant herding algorithm for centroid optimization and then applied the multi-kernel fuzzy c-means clustering for image segmentation. Results: Peak signal-to-noise ratio, mean square error, sensitivity, specificity, and accuracy were used to assess the performance of the proposed methods. According to the findings, the proposed strategy produced better outcomes than the conventional methods. Conclusion: Our proposed methodology was reported to be a more effective technique than existing techniques.
Sreedhar Kollem
Springer Science and Business Media LLC
Sreedhar Kollem
Springer Science and Business Media LLC
Sandip Bhattacharya, Mohammed Imran Hussain, John Ajayan, Shubham Tayal, Louis Maria Irudaya Leo Joseph, Sreedhar Kollem, Usha Desai, Syed Musthak Ahmed, and Ravichander Janapati
Wiley
S. Sreejith, L.M.I. Leo Joseph, Sreedhar Kollem, V.T. Vijumon, and J. Ajayan
Elsevier BV
Sreedhar Kollem, Katta Ramalinga Reddy, and Duggirala Srinivasa Rao
Springer Science and Business Media LLC
Sreedhar Kollem, Katta Ramalinga Reddy, Ch. Rajendra Prasad, Avishek Chakraborty, J. Ajayan, S. Sreejith, Sandip Bhattacharya, L. M. I. Leo Joseph, and Ravichander Janapati
Wiley
J. Ajayan, P. Mohankumar, D. Nirmal, L.M.I. Leo Joseph, Sandip Bhattacharya, S. Sreejith, Sreedhar Kollem, Shashank Rebelli, Shubham Tayal, and B. Mounika
Elsevier BV
Sreedhar Kollem, Ch Rajendra Prasad, J. Ajayan, V. Malathy, and Akkala Subbarao
Springer Science and Business Media LLC
Sreedhar Kollem
IEEE
The classification of MRI brain tumors is crucial in medical image processing. However, current classification techniques are ineffective in differentiating benign from malignant tumors, which raises mortality rates. This article proposes a comprehensive methodology that includes data augmentation, a hybrid CNN model, an optimal feature selection method, and a set of classifiers to increase classification accuracy and decrease mortality rates. The proposed method has four steps. First, Lipschitz-based data augmentation is used to reduce data overfitting problems. Second, in order to improve feature extraction, a hybrid CNN model that is composed of three different CNN models, such as AlexNet, VGG-19, and ResNet-50, is utilized. Thirdly, to choose the best features, the Minimum Redundancy Maximum Relevance approach is used. Finally, for the classification task, a set of classifiers comprising SVM and KNN are used. The results obtained using the proposed method are superior to those obtained using the methods that are currently being utilized.
Sreedhar Kollem
IEEE
Multi-Class MRI Brain Tumor Classification is essential for lowering mortality rates in the field of medical imaging. Nevertheless, current techniques do not distinguish between various tumor forms, including glioma, meningioma, no-tumor, and pituitary tumors. Hyper-parameters like batch size and epochs are not optimized by these approaches. As a result, classification accuracy is affected. This paper provides a uniquely optimized CNN method that combines the pre-trained AlexNet CNN model with the Rat Swarm Optimizer technique in order to overcome the aforementioned constraints. There are three stages to this process. First, images of MRI brain tumors are acquired from the BraTS2020 database, which contains images of pituitary, glioma, meningioma, and no-tumor. Second, the images were classified using the pre-trained AlexNet CNN model. Finally, the hyper-parameters like as batch size and epochs are optimized using a rat swarm optimizer to obtain the best values for improved classification. The effectiveness of the suggested technique is evaluated using the F1-score, sensitivity, specificity, and accuracy metrics. When compared to current methods, the proposed approach performs better.
Ch. Rajendra Prasad, Sami Mohammed, P.Ramchander Rao, Sreedhar Kollem, Srinivas Samala, and Srikanth Yalabaka
IEEE
A brain tumour is a dangerous form of cancer that happens when cells divide in an abnormal way. Recent advances in deep learning have helped the medical imaging sector in the diagnosis of numerous diseases. This paper presents Multiclass MRI Brain Tumour Classification with Deep Transfer Learning. In the proposed model, VGG-16 is employed as a deep transfer learning model. The dataset is collected from the Kaggle brain tumour MRI dataset, which is a combination of three popular brain tumour datasets such as figshare, SARTAJ, and Br35H datasets. The data are prepossessed by rescaling and random brightness and/or contrast by ±20% before applying to the modified VGG-16 model. The proposed model employs minimum computational resources and achieves better results in accuracy, precision, recall, and F1 score.
L.M.I. Leo Joseph, J. Ajayan, Sandip Bhattacharya, and Sreedhar Kollem
Wiley
Rajendra Ch Prasad, Sreedhar Kollem, Ravichander Janapati, Srinivas Samala, and Sandip Bhattacharya
CRC Press
K. Rajkumar, Abbady Saivardhan Reddy, Paladugula Sahith, P. Shiva Krishna Kumar, and Sreedhar Kollem
IEEE
Numerous difficulties arise while denoising images in image processing at present. The article indicates a unique deep convolution neural network-based image denoising (DCNN). This article presents a DCNN to generate the noisy image as opposed to earlier learning-based methodologies. Therefore, it is possible to extract the clean latent image from the deformed image by removing the noise. Compared to other scenarios, this strategy is the most effective. Although some of the already existing systems included machine learning techniques in addition to filters such as wiener, median, and mean filters, they were unable to produce reliable results. Experimental results show that the suggested de-noising methodology outperforms the current denoising techniques. We may say that this strategy will aid in obtaining better results by looking at factors such as PSNR, MSE, training loss, and training accuracy. The conclusions also demonstrate that multiple noises with various noise levels can be suppressed using a single denoising model.
Ramyateja Singamshetty, Sangani Sruthi, Kodati Chandhana, Sreedhar Kollem, and Ch Rajendra Prasad
IEEE
This article presents a modern brain tumor recognition method heavily based on deep neural network techniques. Although huge datasets are challenging to train, deep convolutional neural networks have excelled in many computer vision tasks. The deadliest disease in humans, according to some estimates, is a brain tumor. Anormal cells in the body can or may not spread rapidly depending on the location of the tumor and its predicted growth rate. The dataset consists of brain tumor images. To enhance the performance of the image, a data augmentation technique is used to avoid overfitting issues. Overfitting is the process by which a network learns a function with a very high variance in order to perfectly model the training data. Access to big data is limited in many application domains, including medical image analysis. The main focus of this work is data augmentation, a data-space solution to the problem of limited data. Any techniques that increase the size of the dataset can be loosely referred to as data augmentation. For instance, the image can zoom in or out and save the result, alter the brightness of the image, or rotate it. The obtained data are trained with k-fold, a data partitioning technique that enables making the most of the dataset to create a more comprehensive model. Finally, an inception framework facilitates the ability to train much deeper networks than those previously used. Three image augmentation algorithms are covered in this article: data augmentation, the k-fold model, and the inception v3 model that classifies the images by minimizing overfitting issues. Lastly, there is an application page that contains a code in the backend that predicts whether a person is suffering from a brain tumor or not. Specificity, sensitivity, and accuracy are used to assess the performance of the suggested model.
Akula Shravya Sri, Bobbili Varshith Reddy, Kanuri Balakrishna, Vollala Akshitha, Sreedhar Kollem, and Ch Rajendra Prasad
IEEE
This article presents Multi-modal MRI scans used to classify brain tumors according to their size and imaging appearance. Object detection has been significantly improved by utilizing convolutional neural networks and deep learning approaches, resulting in superior performance. Our solution to tackle uncertainty involves a new deep learning method that incorporates pre-processing techniques such as data augmentation, as well as a customized convolutional neural network. The proposed method aims to achieve three objectives. (1) To address overfitting concerns, pre-processing techniques such as data augmentation are employed. (2) A customized deep learning method is used, which includes a convolutional neural network, to classify brain tumors. (3) A Web APP is utilized to provide information related to the tumor. The suggested procedure's performance is evaluated using sensitivity, specificity, and accuracy, and it outperforms traditional deep learning methods. Additionally, the entire process can be completed on the Python platform.
Ch.Rajendra Prasad, Ramya Bandi, Devulapally Aashrith, Anjali Sampelly, Maraboina Sai Chand, and Sreedhar Kollem
IEEE
Human Activities Recognition is the process of automatically identifying a person’s physical activities in order to create a secure environment for everyone, even elderly people, in their daily lives. In this paper, the classification of human activities using Conventional Neural networks with Principal Component Analysis with presented. In the proposed method, Principal Component Analysis is employed for dimensionality reduction and Conventional Neural networks are employed for classification. The Human Activities Recognition dataset from Kaggle is used in the suggested model. The effectiveness of the proposed model is assessed in terms of accuracy. The proposed model achieved an accuracy of about 96.71%.