The unavailability of appropriate mechanisms for timely detection of diseases and successive treatment causes the death of a large number of people around the globe. The timely diagnosis of grave diseases like different forms of cancer and other life-threatening diseases can save a valuable life or at least extend the life span of an afflicted individual. The advancement of the Internet of Medical Things (IoMT) enabled healthcare technologies can provide effective medical facilities to the population and contribute greatly towards the recuperation of patients. The usage of IoMT in the diagnosis and study of histopathological images can enable real-time identification of diseases and corresponding remedial actions can be taken to save an affected individual. This can be achieved by the use of imaging apparatus with the capacity of auto-analysis of captured images. However, most deep learning-based image classifying models are bulk in size and are inappropriate for use in IoT based imaging devices. The objective of this research work is to design a deep learning-based lightweight model suitable for histopathological image analysis with appreciable accuracy. This paper presents a novel lightweight deep learning-based model "ReducedFireNet", for auto-classification of histopathological images. The proposed method attained a mean accuracy of 96.88% and an F1 score of 0.968 on evaluating an actual histopathological image data set. The results are encouraging, considering the complexity of histopathological images. In addition to the high accuracy the lightweight design (size in few KBs) of the ReducedFireNet model, makes it suitable for IoMT imaging equipment. The simulation results show the proposed model has computational requirement of 0.201 GFLOPS and has a mere size of only 0.391 MB.

由于缺乏及时发现疾病和连续治疗的适当机制，导致全球大量人口死亡。及时诊断严重疾病，如不同形式的癌症和其他危及生命的疾病，可以挽救宝贵的生命，或者至少可以延长患病者的寿命。医疗物联网 (IoMT) 支持的医疗保健技术的进步可以为人们提供有效的医疗设施，并为患者的康复做出巨大贡献。IoMT 在组织病理学图像的诊断和研究中的使用可以实现疾病的实时识别，并可以采取相应的补救措施来挽救受影响的个体。这可以通过使用具有捕获图像自动分析能力的成像设备来实现。然而，大多数基于深度学习的图像分类模型体积庞大，不适合用于基于物联网的成像设备。这项研究工作的目标是设计一种基于深度学习的轻量级模型，适用于具有可观准确性的组织病理学图像分析。本文提出了一种基于深度学习的新型轻量级模型“ReducedFireNet”，用于组织病理学图像的自动分类。所提出的方法在评估实际组织病理学图像数据集时的平均准确率为 96.88%，F1 得分为 0.968。考虑到组织病理学图像的复杂性，结果令人鼓舞。除了高精度之外，ReducedFireNet 模型的轻量级设计（大小为几 KB）使其适用于 IoMT 成像设备。仿真结果表明，所提出的模型具有 0.201 GFLOPS 的计算要求，并且仅具有 0.391 MB 的大小。