A method based on deep neural network optimized with Huffman algorithm and meta-heuristic algorithms for medical image compression and reconstruction

Subject Areas : Computer Engineering and IT

Mohammad Hossein Khalifeh ¹ , Mehdi Taghizadeh ² , Mohammad Mehdi Ghanbarian ³ , جاسم جمالی ⁴

1 - Departmen.t of Electrical Engineering, Kazerun Branch, Islamic Azad University, Kazerun, Iran
2 - هیات علمی
3 - MSc/Islamic Azad University, Kazeroon Branch
4 - دانشگاه آزاد کازرون

Received: 2024-01-04 Accepted : 2024-01-27 Published : 2024-02-08

Keywords: Medical image compression, Image reconstruction, Deep Neural Network, Huffman encryption, Gray Wolf Optimization Algorithm.,

Abstract :

This research makes use of two different approaches to compress medical images for long-term purposes. In the first method, images are compressed using the Huffman cipher and then simplified using a hierarchical modeling based on a neural network-designed categorization. A prediction strategy based on deep neural network training is employed in the second method. This technique uses a trained neural network to infer the locations of individual pixels, hence reducing the amount of data required to describe a picture. Huffman compression encryption is used on the leftover data. An enhanced spatial filtering technique is used to decode the picture data, and the wild horse optimization and gray wolf optimization meta-heuristic algorithms are then used to produce a rebuilt image. Without compromising compression efficiency, this allows for a more realistic application of the suggested solutions in non-deterministic contexts. The suggested approaches allow for picture simplification, which has resulted in faster decoding. Structural similarity index modulation, time and peak signal-to-noise ratio have been improved by an average of 2, 30.1 and 15.15%, respectively. The suggested algorithms were able to compress medical photos with very high quality level, as compared to the current deep learning-based methods.

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