In breast cancer research, diverse data types and formats, such as radiological images, clinical records, histological data, and expression analysis, are employed. Given the intricate nature of natural phenomena, relying on the features of a single modality is seldom sufficient for comprehensive analysis. Therefore, it is possible to guarantee medical relevance and achieve improved clinical outcomes by combining several modalities. The presen study carefully maps and reviews 47 primary articles from six well-known digital libraries that were published between 2018 and 2023 for breast cancer classification based on multimodal deep learning fusion (MDLF) techniques. This systematic literature review encompasses various aspects, including the medical modalities combined, the datasets utilized in these studies, the techniques, models, and architectures used in MDLF and it also discusses the advantages and limitations of each approach. The analysis of selected papers has revealed a compelling trend: the emergence of new modalities and combinations that were previously unexplored in the context of breast cancer classification. This exploration has not only expanded the scope of predictive models but also introduced fresh perspectives for addressing diverse targets, ranging from screening to diagnosis and prognosis. The practical advantages of MDLF are evident in its ability to enhance the predictive capabilities of machine learning models, resulting in improved accuracy across diverse applications. The prevalence of deep learning models underscores their success in autonomously discerning complex patterns, offering a substantial departure from traditional machine learning approaches. Furthermore, the paper explores the challenges and future directions in this field, including the need for larger datasets, the use of ensemble learning methods, and the interpretation of multimodal models.

Visual evaluation of many magnetic resonance images is a difficult task. Therefore,computer‐assisted brain tumor classification techniques have been proposed.These techniques have several drawbacks or limitations. Capsule based neuralnetworks are new approaches that can preserve spatial relationships of learnedfeatures using dynamic routing algorithm. By this way, not only performance oftumor recognition increases but also sampling efficiency and generalisationcapability improves. Therefore, in this work, a Capsule Network (CapsNet) isused to achieve fully automated classification of tumors from brain magneticresonance images. In this work, prevalent three types of tumors (pituitary,glioma and meningioma) have been handled. The main contributions in this paperare as follows: 1) A comprehensive review on CapsNet based methods is presented.2) A new CapsNet topology is designed by using a Sobolev gradient‐basedoptimisation, expectation‐maximisation based dynamic routing and tumor boundaryinformation. 3) The network topology is applied to categorise three types ofbrain tumors. 4) Comparative evaluations of the results obtained by othermethods are performed. According to the experimental results, the proposedCapsNet based technique can achieve extraction of desired features from imagedata sets and provides tumor classification automatically with 92.65%accuracy.