Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization

Abstract

Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated with this type of cancer can be reduced with early detection. Nonetheless, a skilled professional is always necessary to manually diagnose this malignancy from mammography images. Many researchers have proposed several approaches based on artificial intelligence. However, they still face several obstacles, such as overlapping cancerous and noncancerous regions, extracting irrelevant features, and inadequate training models. In this paper, we developed a novel computationally automated biological mechanism for categorizing breast cancer. Using a new optimization approach based on the Advanced Al-Biruni Earth Radius (ABER) optimization algorithm, a boosting to the classification of breast cancer cases is realized. The stages of the proposed framework include data augmentation, feature extraction using AlexNet based on transfer learning, and optimized classification using a convolutional neural network (CNN). Using transfer learning and optimized CNN for classification improved the accuracy when the results are compared to recent approaches. Two publicly available datasets are utilized to evaluate the proposed framework, and the average classification accuracy is 97.95%. To ensure the statistical significance and difference between the proposed methodology, additional tests are conducted, such as analysis of variance (ANOVA) and Wilcoxon, in addition to evaluating various statistical analysis metrics. The results of these tests emphasized the effectiveness and statistical difference of the proposed methodology compared to current methods.

Deep hybrid model for Mpox disease diagnosis from skin lesion images

“…Furthermore, novel approaches integrating AI‐based optimization techniques have been proposed for MPox diagnosis. These approaches involve fine‐tuning customized CNN layers using AI‐Biruni Earth Radius (BER) 3 optimization‐based stochastic fractal search (BERSFS) 22 and employing optimization algorithms like sine cosine (SC) and particle swarm (PS) for feature selection with multilayer perceptron (MLP) classifiers. Additionally, several studies utilized various CNN architectures 13 to differentiate between MPox and non‐MPox cases using both MSID and MSLD datasets.…”

“…The detection and diagnosis of human MPox [19][20][21] have received increasing attention in the scientific literature, particularly with the recent global spread of MPox infection. However, research is insufficient in applying CAD 22 for primary diagnosis due to the limited availability of publicly accessible datasets for training and testing purposes. CNNs 23 have demonstrated remarkable ability in analyzing medical images and identifying patterns related to various diseases, making them promising candidates for MPox diagnosis.…”

Adaptive Multi-source Domain Collaborative Fine-tuning for Transfer Learning

“…This approach has achieved significant success in a variety of tasks for which training data is insufficient or of poor quality [1,2]. Model fine-tuning, as a commonly used transfer learning technique, effectively improves the learning performance by transferring the parameters of the pre-trained neural network (NN) model from the source domain to the target task and fine-tuning them [3,4]. Such fine-tuning techniques are already successfully used in various fields such as computer vision [5], natural language processing [6], speech recognition [7], recommendation systems [8], and medical diagnosis [9].…”