Bio inspired optimization techniques for disease detection in deep learning systems

Abstract

Numerous contemporary computer-aided disease detection methodologies predominantly depend on feature engineering techniques; yet, they possess several drawbacks, including the presence of redundant features and excessive time consumption. Conventional feature engineering necessitates considerable manual effort, resulting in issues from superfluous features that diminish the model's performance potential. In contrast to recent effective deep-learning models, these may address these issues while concurrently obtaining and capturing intricate structures inside extensive medical image datasets. Deep learning models autonomously develop feature extraction abilities but require substantial computational resources and extensive datasets to yield significant abstraction methods. The dimensionality problem is a key challenge in healthcare research. Despite the hopeful advancements in illness identification with deep learning architectures in recent years, attaining high performance remains notably tough, particularly in scenarios with limited data or intricate feature spaces. This research endeavors to elucidate the integration of bio-inspired optimization techniques that improve disease diagnostics through deep learning models. The targeted feature selection of bio-inspired methods enhances computational efficiency and operational efficacy by minimizing model redundancy and computational costs, particularly when data availability is constrained. These algorithms employ natural selection and social behavior models to efficiently explore feature spaces, enhancing the robustness and generalizability of deep learning systems. This paper seeks to elucidate the efficacy of deep learning models in medical diagnostics by employing concepts and strategies derived from biological system ontologies, such as genetic algorithms, particle swarm optimization, ant colony optimization, artificial immune systems, and swarm intelligence. Bio-inspired methodologies have exhibited significant potential in addressing critical challenges in illness detection across many data types. It seeks to tackle the problem by creating bio-inspired optimization methods to enhance efficient and equitable deep learning for illness diagnosis. This work assists researchers in selecting the most effective bio-inspired algorithm for disease categorization, prediction, and the analysis of high-dimensional biomedical data.

Keywords: Algorithms; Bio-inspired deep learning models; Bioinformatics; Biomedical data; Classification; Disease detection; Diseases; Healthcare; Machine learning.

Fig. 1

Working mechanism of genetic algorithm.

Fig. 2

Working mechanism of particle swarm optimization.

Fig. 3

Working mechanism of ant colony optimization algorithm.

Fig. 4

Working mechanism of GWO algorithm.

Fig. 5

Working mechanism of memetic algorithm.

Fig. 6

Working mechanism of WOA.

Fig. 7

Overall flow diagram of bioinspired optimization algorithms.

Fig. 8

Breast cancer images.

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Lung cancer images.

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Diabetes images.

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Alzheimer’s images.

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Heart disease images.

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COVID 19 images.

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Skin tumour images.

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Stroke affected images.

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Prostate cancer images.

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Osteoporosis images.

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Pancreatic cancer images.

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Colorectal cancer images.

Fig. 20

Breast cancer- performance comparison.

Fig. 21

Lung cancer- performance comparison.

Fig. 22

Diabetes- performance comparison.

Fig. 23

Alzheimer’s disease- performance comparison.

Fig. 24

Heart disease- performance comparison.

Fig. 25

Covid- performance comparison.

Fig. 26

Skin cancer- performance comparison.

Fig. 27

Stroke detection- performance Comparison.

Fig. 28

Prostate cancer- performance comparison.

Fig. 29

HIV/AIDS- performance comparison.

Fig. 30

Osteoporosis- performance comparison.

Fig. 31

Pancreatic cancer- performance comparison.

Fig. 32

Colorectal cancer- performance comparison.

Fig. 33

Hepatitis- performance comparison.

Fig. 34

Obesity- performance comparison.

Fig. 35

Asthma prediction- performance comparison.