GI Tract Lesion Classification Using Multi-task Capsule Networks with Hierarchical Convolutional Layers

Ali, Mohammed Farid; Li, Chao; He, Kun

doi:10.1007/978-3-031-20233-9_65

Cited by 1 publication

(1 citation statement)

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“…Conducting a thorough ablation study is imperative to ensure the model's robustness and prevent the generation of misleading results [40]. In this context, our study encompasses a comprehensive exploration employing the following criteria.…”

Section: Ablation Studymentioning

confidence: 99%

Dual-input Dual-stream Network with Two-fold Margin Loss for Improved Brain Tumor Detection on MRI Images

Tole,

Kaloi,

Ali

et al. 2024

Preprint

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Brain tumor being one of the major health hazards always needs a prompt diagnosis for the early treatment options to improve the chances of the survival of the patients. Traditional manual assessment of MRI (magnetic resonance imaging) to identify these conditions is a common practice. Hence, automation of the diagnosis practices can considerably improve the quality of the treatment procedures. In recent years almost every method for the automated diagnosis of brain tumor detection uses a deep learning technique. Despite decent works current techniques need a significant improvement to produce better results. Along with failure to produce decent results, the current deep learning models present very complex architectures which sometimes require huge computational resources. Therefore, in this paper we present a novel method that uses a lightweight dual-stream model with dual-input to detect brain tumors on MRI images. Both the inputs use different pre-processing mechanisms based on Contrast Limited Adaptive Histogram Equalization (CLAHE) and White Patch Retinex algorithm in order to enhance the feature learning capabilities of the proposed model. In addition, the proposed method uses a two-fold margin loss to boost training process for better feature learning. The proposed method produces state-of-the-art results for the detection of Glioma, Meningioma and Pituitary tumors. The model presents accuracy of 99%, 98% and 99% respectively for Glioma, Meningioma and Pituitary tumor detection.

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Section: Ablation Studymentioning

confidence: 99%