Cognitive Computing-Based Mammographic Image Classification on an Internet of Medical

Abstract

Recently, the Internet of Medical Things (IoMT) has become a research hotspot due to its various applicability in medical field. However, the data analysis and management in IoMT remain challenging owing to the existence of a massive number of devices linked to the server environment, generating a massive quantity of healthcare data. In such cases, cognitive computing can be employed that uses many intelligent technologies-machine learning (ML), deep learning (DL), artificial intelligence (AI), natural language processing (NLP) and others-to comprehend data expansively. Furthermore, breast cancer (BC) has been found to be a major cause of mortality among ladies globally. Earlier detection and classification of BC using digital mammograms can decrease the mortality rate. This paper presents a novel deep learning-enabled multi-objective mayfly optimization algorithm (DL-MOMFO) for BC diagnosis and classification in the IoMT environment. The goal of this paper is to integrate deep learning (DL) and cognitive computing-based techniques for e-healthcare applications as a part of IoMT technology to detect and classify BC. The proposed DL-MOMFO algorithm involved Adaptive Weighted Mean Filter (AWMF)-based noise removal and contrast-limited adaptive histogram equalisation (CLAHE)-based contrast improvement techniques to improve the quality of the digital mammograms. In addition, a U-Net architecture-based segmentation method was utilised to detect diseased regions in the mammograms. Moreover, a SqueezeNet-based feature extraction and a fuzzy support vector machine (FSVM) classifier were used in the presented technique. To enhance the diagnostic performance of the presented method, the MOMFO algorithm was used to effectively tune the parameters of the SqueezeNet and FSVM techniques. The DL-MOMFO technique was tested on the MIAS database, and the experimental outcomes revealed that the DL-MOMFO technique outperformed existing techniques.

Detection of Breast Cancer through the Analysis of Radiographic Images Using Machine Learning: A Systematic Review

“…Given this, it is deduced that ML is a reliable network for detecting cancer that was not detected by the initial reader [17]. It was also observed that ML provides support for radiologists, according to several articles [1][2][3], [6], [7], [11], [17], [18], [20][21][22][23], [29], so it is deduced to be the main advantage since it is the most mentioned in the reviewed articles. ML would help doctors build a comprehensive and personalized view of each patient and assist in making judgments and decisions [11].…”

“…Finally, the number of articles was reduced to 26, which will be used to develop the systematic literature review. From the different databases, eight articles published during the year 2023 were identified [1], [3], [4], [6], [11], [13], [14], along with eight articles from the year 2022 [2], [5], [7][8][9][10], [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29], all of which met the filters established in the methodology, and that is why they were selected. The will above aids in conducting the review with data from up-to-date research grounded in knowledge from subsequent years.…”

“…Furthermore, in Figure 5, the identified advantages in the articles [1][2][3], [5][6][7], [11], [17], [18], [20][21][22][23][24], [29] were grouped. These studies found five advantages, with ML as a support for radiologists being the most mentioned and, therefore, the most significant advantage found.…”

“…Another limitation of ML, according to studies [2], [7], [9], [11], [13], [14], [21][22][23][24][25][26][27][28][29], is related to data. When the amount of data increases for ML-based systems, it becomes evident that the algorithm faces issues that prevent it from functioning correctly [1]; the performance of ML-based algorithms is considered conventional [9].…”

Machine learning and new insights for breast cancer diagnosis

“…28 This current review extracted and organized the data in tabular form and summarized the use of MG in the diagnosis of breast cancer (Table 1). 2,4,5,7,9,24,29–79…”