Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Spectral Indices based Land Cover Classification using Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2024) Payani, C.A.; Gupta, C.; Anand Kumar, M.In this paper, we use Landsat 8 and 9 satellite data to predict the land area that is suitable for agriculture and farming. Early identification and deriving insights from areas and their land properties will give us the scope for better utilization of the area. To achieve this, We used 2 manually created datasets using google earth engine. Even though the main motive is to predict the productive cropland using the created dataset. classification task we intended is to identify the type of region in the given area of land as Water, Barren land, cropland, forests, and urban areas. Deep feed forward neural network and 1D CNN models are used for this classification. The DFNN model consistently outperformed the 1D CNN across all datasets, showing superior classification accuracy and overall performance. On both the KAPLCU, MLCU, and Hybrid datasets, DFNN demonstrated better precision, recall, and F1 scores, confirming its effectiveness in classifying land regions based on satellite imagery. Future work could involve exploring land cover classification using government datasets and developing labeled repositories from unlabeled satellite images to further expand research potential in this domain. © 2024 IEEE.Item Osteosarcoma Bone Cancer Detection(Springer Science and Business Media Deutschland GmbH, 2025) Payani, C.A.; Gupta, C.; Vamsidhar, K.; Bhat, P.; Patil, N.Osteosarcoma is a type of bone cancer commonly found in the elongated bones found in the upper and lower limbs. The precise cause is unknown, but experts believe it’s linked to changes in the DNA of the bones, resulting in the growth of abnormal and harmful bone tissue. If caught early, osteosarcoma is treatable, with about 75% people cured when the cancer hasn’t spread to other body parts. Analyzing biopsy samples can be time-consuming, but there are advanced computer programs, known as supervised deep learning methods, that can help speed up the process and enhance the efficiency of the diagnosis. Previous studies have already evaluated the performance of deep learning models such as VGG16, VGG19, DenseNet201, and ResNet101, among which ResNet101 performed better with 90.36% accuracy. When it comes to understanding complex image features, previous models lack behind. We propose EfficientNetV2, Xception, and InceptionV3 models, among which Xception outperformed other models with 94.5% accuracy on the image dataset. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.