Integration of Genetic Algorithm and Convolutional Neural Networks for Histopathological Image Analysis in Breast Cancer Diagnosis
Keywords:
Breast cancer diagnosis, Genetic Algorithm, CNN, Deep learning, Disease diagnosisAbstract
Histopathological images frequently have complex patterns and structures that are difficult for conventional image processing methods to effectively analyse. In image classification applications, CNNs have demonstrated considerable potential, but their performance can be greatly influenced by the choice of the best hyperparameters, such as the depth of the architecture and the kind of filters to use. In this study, genetic algorithms are used to automatically find these ideal hyperparameters, enhancing the CNN's capability to detect breast cancer. The proposed hybrid model optimises the CNN architecture by utilising the evolutionary search capabilities of GAs, allowing it to successfully extract pertinent features and patterns from histopathology pictures. A CNN that is better suited for breast cancer categorization is produced by this dynamic optimisation process, increasing diagnostic precision. On a sizable collection of histopathology imaging data, rigorous tests were carried out to assess the efficacy of our method. Comparing the results to conventional CNN models, the findings show a considerable improvement in diagnosis accuracy. Additionally, the model is easier to use and more effective because to the incorporation of GAs, which also minimises the need for manual hyperparameter adjustment. In conclusion, a promising method for enhancing breast cancer diagnosis using histopathological image analysis is the combination of genetic algorithms with convolutional neural networks. This hybrid model's automated hyperparameter optimisation procedure offers precise and effective diagnostic abilities, ultimately improving patient outcomes in the area of breast cancer diagnosis and treatment.
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