Deep learning solutions for inverse problems in advanced biomedical image analysis on disease detection.

Area of Science:

• Biomedical Image Analysis
• Computational Pathology
• Medical Imaging Informatics

Background:

• Inverse problems are crucial in biomedical imaging for deducing tissue properties from observed data.
• Accurate disease detection relies on analyzing complex data from modalities like MRI and CT scans.
• Deep learning offers robust solutions for inverse problems in biomedical image analysis.

Purpose of the Study:

• To develop a deep learning solution for inverse problems in advanced biomedical image analysis for disease detection.
• To introduce the DLSIP-ABIADD technique for solving inverse problems and identifying diseases in medical images.
• To enhance diagnostic efficiency and enable early disease detection through computational methodologies.

Main Methods:

• The DLSIP-ABIADD technique employs a direct mapping approach to solve inverse problems.
• Image preprocessing utilizes Bilateral Filtering (BF).
• Feature extraction is performed using MobileNetv2, with hyperparameter optimization via Henry gas solubility optimization (HGSO). Disease identification is achieved using a bidirectional long short-term memory (BiLSTM) model.

Main Results:

• The DLSIP-ABIADD technique demonstrated superior performance compared to existing models in simulations.
• Extensive experimentation validated the effectiveness of the proposed deep learning approach.
• The technique accurately identifies diseases by solving inverse problems in biomedical images.

Conclusions:

• The DLSIP-ABIADD technique offers a promising deep learning-based approach for inverse problems in biomedical image analysis.
• This method significantly improves disease detection accuracy and diagnostic efficiency.
• The study highlights the potential of advanced computational techniques for early and precise disease identification.