A CNN Hyperparameters Optimization Based on Particle Swarm Optimization for Mammography Breast Cancer Classification

Abstract

Breast cancer is considered one of the most-common types of cancers among females in the world, with a high mortality rate. Medical imaging is still one of the most-reliable tools to detect breast cancer. Unfortunately, manual image detection takes much time. This paper proposes a new deep learning method based on Convolutional Neural Networks (CNNs). Convolutional Neural Networks are widely used for image classification. However, the determination process for accurate hyperparameters and architectures is still a challenging task. In this work, a highly accurate CNN model to detect breast cancer by mammography was developed. The proposed method is based on the Particle Swarm Optimization (PSO) algorithm in order to look for suitable hyperparameters and the architecture for the CNN model. The CNN model using PSO achieved success rates of 98.23% and 97.98% on the DDSM and MIAS datasets, respectively. The experimental results proved that the proposed CNN model gave the best accuracy values in comparison with other studies in the field. As a result, CNN models for mammography classification can now be created automatically. The proposed method can be considered as a powerful technique for breast cancer prediction.

Artificial Intelligence in Cutaneous Leishmaniasis Diagnosis: Current Developments and Future Perspectives

“…Recent strides in AI have had a transformative impact on the field of medicine, particularly with the utilization of Machine/Deep Learning (ML/DL) algorithms for enhanced diagnosis capabilities, treatment planning, patient monitoring, and the personalization of medical care [26,27]. Convolutional Neural Networks (CNNs), a type of DL, have proven successful in classifying image data, notably in dermatology, radiology [28], and pathology [29]. Studies leveraging DL models for dermatological diagnosis, such as those by Han et al [30], Choy et al [31], Srinivasu et al [32], Goceri [33], and AlSuwaidan [34], underscore the potential of CNNs in achieving accurate diagnosis, even outperforming dermatologists in certain scenarios.…”