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Classifying Kidney Tumor via Adaptive SE-ResNeXt With Mutli-Novel Loss Function and Renal Mass Segmentation Using
Bipin Bihari Jayasingh1, H Niroshini Infantia2, Tejaswini Panse3
1IT Department, CVR College of Engineering, Hyderabad, Telangana, India.
Abstract:
Kidney tumors are one of the prevalent types of tumor globally and it has become a significant health concern. It is the most frequent type of urological tumor. While kidney tumors can present in various forms, the majority of the tumors are malignant. Hence, early detection is needed to reduce the mortality rates, develop preventive measures to minimize their impact and provide effective treatments. Compared to traditional, labor-intensive diagnostic process, deep learning-based automatic detection algorithms can reduce diagnosis time, lower costs and improve accuracy. In this research, a kidney cancer detection model using deep learning is introduced to speed up the diagnosis process with high efficiency. At first, the input images are taken from benchmark sites. Then, segmentation is performed on the input images using developed Pyramidal Attention-based Recurrent Residual Unet++ (PA-R2Unet++) model. The development of a tumor is identified in the early stages by this accurate segmentation procedure. Then, the segmented images are used in the classification procedure and it is accomplished via Adaptive Squeeze-And-Excitation-ResNeXt with Novel Multi-Loss Function (ASE-RX-NMLF). In addition, an Enhanced Arbitrary Value-based Flamingo Search Algorithm (EAV-FSA) is used to tune the parameters from the suggested ASE-RX-NMLF for improving the classification performance. Classifying kidney tumors based on their characteristics helps to enhance the effectiveness of therapies. The efficacy of the developed method is verified by comparing the final results of existing models.
Insights
This study introduces a deep learning model for efficient kidney cancer detection. The model accurately segments and classifies tumors, aiding in early diagnosis and treatment planning.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Kidney tumors are a significant global health concern, with malignant forms requiring early detection for improved outcomes.
- Traditional diagnostic methods for kidney tumors are labor-intensive and can be time-consuming.
- Deep learning offers a promising approach to automate and enhance the accuracy of kidney cancer diagnosis.
Purpose of the Study:
- To develop and evaluate a deep learning-based model for the efficient and accurate detection of kidney tumors.
- To improve the speed and reduce the cost of kidney cancer diagnosis through automated algorithms.
- To classify kidney tumors based on their characteristics to personalize and enhance therapeutic effectiveness.
Main Methods:
- Utilized a Pyramidal Attention-based Recurrent Residual Unet++ (PA-R2Unet++) model for precise image segmentation of kidney tumors.
- Employed an Adaptive Squeeze-And-Excitation-ResNeXt with Novel Multi-Loss Function (ASE-RX-NMLF) for tumor classification.
- Optimized the classification model parameters using an Enhanced Arbitrary Value-based Flamingo Search Algorithm (EAV-FSA).
Main Results:
- The developed PA-R2Unet++ model achieved accurate segmentation, enabling early identification of tumor development.
- The ASE-RX-NMLF model, optimized by EAV-FSA, demonstrated high efficiency in classifying kidney tumors.
- The proposed deep learning approach showed comparable or superior efficacy to existing models in kidney tumor detection.
Conclusions:
- The introduced deep learning model significantly speeds up the kidney cancer diagnosis process.
- Accurate segmentation and classification are crucial for early detection and effective treatment of kidney tumors.
- This automated approach holds potential for reducing healthcare costs and improving patient outcomes in urological oncology.