ICTACT Journal on Image and Video Processing

Shweta Nishit Jain ¹, Priya Pise ², Akhilesh Mishra ³

Affiliations
1 Department of Electronics and Communication, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
2 Department of Computer Science and Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
3 Department of Electrical and Electronics Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN

Abstract

This research presents a novel approach for feature extraction in image understanding, utilizing an AT-ConvLSTM-based Cultural Algorithm. The Proposed CA-AT-ConvLSTM leverages the power of deep learning through AT-ConvLSTM architecture while optimizing the feature extraction process using Cultural Algorithms. This synergistic approach enhances the efficiency and accuracy of image understanding tasks, making it suitable for a wide range of applications, from computer vision to pattern recognition. The experimental results demonstrate the superiority of the proposed technique over traditional methods, highlighting its potential in advancing the field of image analysis.

Keywords

Feature Extraction, AT-ConvLSTM, Cultural Algorithm, Image Understanding, Deep learning

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Shweta Nishit Jain ¹, Priya Pise ², Akhilesh Mishra ³

Affiliations
1 Department of Electronics and Communication Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
2 Department of Computer Science and Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN
3 Department of Electrical and Electronics Engineering, Shri Jagdishprasad Jhabarmal Tibrewala University, IN

Abstract

Medical image analysis plays a pivotal role in modern healthcare, aiding clinicians in accurate diagnosis and treatment planning. However, the complexity and diversity of medical images pose significant challenges for traditional image processing methods. Existing methods often struggle to precisely delineate structures in medical images, leading to suboptimal diagnostic accuracy. The demand for automated and accurate segmentation tools in medical imaging has grown, highlighting the necessity for robust and efficient algorithms capable of handling diverse anatomical variations and pathologies. While CNNs have shown promise in image analysis, their application to medical images requires customization to accommodate unique challenges. The literature lacks comprehensive studies that bridge the gap between general-purpose CNNs and the specific demands of medical image segmentation, especially concerning the diverse and intricate structures present in medical imagery. This study addresses the need for advanced techniques by leveraging Convolutional Neural Networks (CNNs) for semantic segmentation in medical image analysis. Our approach involves the design and implementation of a specialized CNN architecture tailored to the nuances of medical image data. We employ state-of-the-art techniques for data preprocessing, model training, and validation. The model is trained on a diverse dataset encompassing various medical imaging modalities, ensuring its adaptability and generalizability. The proposed CNN-based semantic segmentation model demonstrates superior performance in accurately delineating anatomical structures compared to traditional methods. Evaluation metrics, including Dice coefficient and sensitivity, indicate the model efficacy in achieving precise segmentation. The results underscore the potential of CNNs in advancing medical image analysis for improved clinical outcomes.

Keywords

Convolutional Neural Networks, Medical Image Analysis, Semantic Segmentation, Anatomical Structures, Automated Diagnosis

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