Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Optimized Deep Learning for Medical Image Retrieval and Classification

Optimized Deep Learning for Medical Image Retrieval and Classification

Ch, Bindu Madhuri
D, Bhanu Mahesh
D, Rajya Lakshmi

Paperback

Technology & Engineering

ISBN10: 6207479777
ISBN13: 9786207479771
Publisher: LAP Lambert Academic Publishing
Published: May 17 2026
Pages: 200
Weight: 0.60
Height: 0.46 Width: 6.00 Depth: 9.00
Language: English
Computer Aided Diagnosis (CAD) has been a key research area for a decade. CAD systems can help to detect and classify diseases by automating/semi-automating medical image analysis. Computer Aided Diagnosis deals with different medical image modalities and comprises two main functionalities. One is image retrieval, i.e. retrieving similar kinds of images and the second is classification for a given input image. Medical images come in various modalities (e.g., X-ray, MRI, CT scans) and formats, leading to challenges in developing a unified approach for feature extraction, retrieval, and classification across diverse image types which are essential for developing CAD systems. Traditional retrieval methods such as text annotation and histogram-based approaches suffer from several critical drawbacks. This work focuses on developing Content-Based Medical Image Retrieval (CBMIR) and Classification models which can be used in developing efficient CAD systems and in turn can provide complete assistance to radiologists and doctors in diagnosing diseases effectively.

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