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Segmentation of suspicious regions (SRs) of a thermal breast image (TBI) is a very significant and challenging problem for identification of breast cancer. Therefore, in this work, we have proposed an active contour model for the segmentation of the SRs in a TBI. The proposed segmentation method combines three significant steps. First, a novel method, called smaller-peaks corresponding to the high-intensity-pixels and the centroid-knowledge of SRs (SCH-CS), is proposed to approximately locate the SRs, whose contours are later used as the initial evolving curves of the level set method (LSM). Second, a new energy functional, called different local priorities embedded (DLPE), is proposed regarding the level set function. DLPE is then minimized using the interleaved level set evolution to segment the potential SRs in a TBI more accurately. Finally, a new stopping criterion is incorporated into the proposed LSM. The proposed LSM not only increases the segmentation speed but also ameliorates the segmentation accuracy. Performance of our SR segmentation method was evaluated on two TBI databases, namely, DMR-IR and DBT-TU-JU and the average segmentation accuracies obtained on these databases are 72.18% and 71.26% respectively, which are better than other state-of-the-art methods. Beside this, a novel framework to analyze TBIs is proposed for differentiating abnormal and normal breasts on the basis of the segmented SRs. We have also shown experimentally that investigating only the SRs instead of the whole breast is more effective in differentiating abnormal and normal breasts.

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PurposeIn recent times, due to rapid urbanization and the expansion of the E-commerce industry, drone delivery has become a point of interest for many researchers and industry practitioners. Several factors are directly or indirectly responsible for adopting drone delivery, such as customer expectations, delivery urgency and flexibility to name a few. As the traditional mode of delivery has some potential drawbacks to deliver medical supplies in both rural and urban settings, unmanned aerial vehicles can be considered as an alternative to overcome the difficulties. For this reason, drones are incorporated in the healthcare supply chain to transport lifesaving essential medicine or blood within a very short time. However, since there are numerous types of drones with varying characteristics such as flight distance, payload-carrying capacity, battery power, etc., selecting an optimal drone for a particular scenario becomes a major challenge for the decision-makers. To fill this void, a decision support model has been developed to select an optimal drone for two specific scenarios related to medical supplies delivery.Design/methodology/approachThe authors proposed a methodology that incorporates graph theory and matrix approach (GTMA) to select an optimal drone for two specific scenarios related to medical supplies delivery at (1) urban areas and (2) rural/remote areas based on a set of criteria and sub-criteria critical for successful drone implementation.FindingsThe findings of this study indicate that drones equipped with payload handling capacity and package handling flexibility get more preference in urban region scenarios. In contrast, drones with longer flight distances are prioritized most often for disaster case scenarios where the road communication system is either destroyed or inaccessible.Research limitations/implicationsThe methodology formulated in this paper has implications in both academic and industrial settings. This study addresses critical gaps in the existing literature by formulating a mathematical model to find the most suitable drone for a specific scenario based on its criteria and sub-criteria rather than considering a fleet of drones is always at one's disposal.Practical implicationsThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.Social implicationsThe proposed methodology incorporates GTMA to assist decision-makers in order to appropriately choose a particular drone based on its characteristics crucial for that scenario.Originality/valueThis research will serve as a guideline for the practitioners to select the optimal drone in different scenarios related to medical supplies delivery.

Medical images mostly suffer from data imbalance problems, which make the disease classification task very difficult. The imbalanced distribution of the data in medical datasets happens when a proportion of a specific type of disease in a dataset appears in a small section of the entire dataset. So analyzing medical datasets with imbalanced data is a significant challenge for the machine learning and deep learning community. A standard classification learning algorithm might be biased towards the majority class and ignore the importance of the minority class (class of interest), which generally leads to the wrong diagnosis of the patients. So, the data imbalance problem in the medical image dataset is of utmost importance for the early prediction of disease, specifically cancer. This chapter attempts to explore different problems concerning data imbalance in medical diagnosis. The authors have discussed different rebalancing strategies that offer guidelines for choosing appropriate optimal procedures to train the samples by a classifier for an efficient medical diagnosis.

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