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International Journal for Research Trends and Innovation
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ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2025
Volume 10 | Issue 4
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Impact Factor : 8.14
Issue per Year : 12
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Issue Published : 107
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| Paper Title: | An Improvised Deep Learning Model with Loss functions Fusion for Image Classification |
| Authors Name: | Kavyadarshnee K S , Sreenithi B , Devnath R , Sethu Vignesh S , Dr. Geetha Ramani |
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IJRTI_200934
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| Published Paper Id: | IJRTI2502111 |
| Published In: | Volume 10 Issue 2, February-2025 |
| DOI: | |
| Abstract: | Deep learning has transformed image classification by enabling models like CNNs to automatically learn features from data, overcoming the limitations of traditional manual feature extraction methods. The VGG architec ture, known for its simplicity and effectiveness, is widely used in various image classification tasks. This project aims to enhance image classification accuracy by leveraging a fu sion of multiple loss functions using U-Net, VGG16, and CNN models across datasets like the DeepGlobe Land Cover Dataset, BraTS Brain Tumor Dataset, Tomato Leaf Disease Dataset, and Crack Segmentation Dataset. This project comprises preprocessing of images, training models with the fusion of loss functions, and evaluating their performance against key metrics. A novel ap proach is proposed to combine loss functions such as Cross-Entropy, Focal Loss, Tversky Loss, Dice Loss, and Smooth L1 Loss to address challenges in convergence and performance, especially with complex and imbalanced da tasets. Different activation functions and n-gram combinations were explored to further enhance the model's ability to generalize and learn from the data. These techniques aim to create a more robust and versatile model capable of handling diverse image classification tasks. The development of this project was carried out using Python as the primary programming language, with extensive use of frameworks such as TensorFlow and PyTorch for model implementation and training. Google Colab served as the primary platform for experimentation and model training, leveraging its GPU support for efficient computations. |
| Keywords: | Dice Loss, Loss Function Fusion, Image Classification, Deep Learning. |
| Cite Article: | "An Improvised Deep Learning Model with Loss functions Fusion for Image Classification ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 2, page no.b87-b100, February-2025, Available :http://www.ijrti.org/papers/IJRTI2502111.pdf |
| Downloads: | 00078 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2502111
Registration ID:200934
Published In: Volume 10 Issue 2, February-2025
DOI (Digital Object Identifier):
Page No: b87-b100 Country: Chennai, Tamil Nadu, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2502111 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2502111 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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