Breast cancer is a fatal disease and is a leading cause of death in women worldwide. The process of diagnosis based on biopsy tissue is nontrivial, time-consuming, and prone to human error, and there may be conflict about the final diagnosis due to interobserver variability. Computer-aided diagnosis systems have been designed and implemented to combat these issues. These systems contribute significantly to increasing the efficiency and accuracy and reducing the cost of diagnosis. Moreover, these systems must perform better so that their determined diagnosis can be more reliable. This research investigates the application of the EfficientNet architecture for the classification of hematoxylin and eosin-stained breast cancer histology images provided by the ICIAR2018 dataset. Specifically, seven EfficientNets were fine-tuned and evaluated on their ability to classify images into four classes: normal, benign, in situ carcinoma, and invasive carcinoma. Moreover, two standard stain normalization techniques, Reinhard and Macenko, were observed to measure the impact of stain normalization on performance. The outcome of this approach reveals that the EfficientNet-B2 model yielded an accuracy and sensitivity of 98.33% using Reinhard stain normalization method on the training images and an accuracy and sensitivity of 96.67% using the Macenko stain normalization method. These satisfactory results indicate that transferring generic features from natural images to medical images through fine-tuning on EfficientNets can achieve satisfactory results.

The bin-packing problem (BPP) is an age-old NP-hard combinatorial optimization problem, which is defined as the placement of a set of different-sized items into identical bins such that the number of containers used is optimally minimized. Besides, different variations of the problem do exist in practice depending on the bins dimension, placement constraints, and priority. More so, there are several important real-world applications of the BPP, especially in cutting industries, transportation, warehousing, and supply chain management. Due to the practical relevance of this problem, researchers are consistently investigating new and improved techniques to solve the problem optimally. Nature-inspired metaheuristics are powerful algorithms that have proven their incredible capability of solving challenging and complex optimization problems, including several variants of BPPs. However, no comprehensive literature review exists on the applications of the metaheuristic approaches to solve the BPPs. Therefore, to fill this gap, this article presents a survey of the recent advances achieved for the one-dimensional BPP, with specific emphasis on population-based metaheuristic algorithms. We believe that this article can serve as a reference guide for researchers to explore and develop more robust state-of-the-art metaheuristics algorithms for solving the emerging variants of the bin-parking problems.

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