In view of the recent paradigm shift in deep AI based image processing methods, medical image processing has advanced considerably. In this study, we propose a novel deep neural network (DNN), entitled InceptNet, in the scope of medical image processing, for early disease detection and segmentation of medical images in order to enhance precision and performance. We also investigate the interaction of users with the InceptNet application to present a comprehensive application including the background processes, and foreground interactions with users. Fast InceptNet is shaped by the prominent Unet architecture, and it seizes the power of an Inception module to be fast and cost effective while aiming to approximate an optimal local sparse structure. Adding Inception modules with various parallel kernel sizes can improve the network's ability to capture the variations in the scaled regions of interest. To experiment, the model is tested on four benchmark datasets, including retina blood vessel segmentation, lung nodule segmentation, skin lesion segmentation, and breast cancer cell detection. The improvement was more significant on images with small scale structures. The proposed method improved the accuracy from 0.9531, 0.8900, 0.9872, and 0.9881 to 0.9555, 0.9510, 0.9945, and 0.9945 on the mentioned datasets, respectively, which show outperforming of the proposed method over the previous works. Furthermore, by exploring the procedure from start to end, individuals who have utilized a trial edition of InceptNet, in the form of a complete application, are presented with thirteen multiple choice questions in order to assess the proposed method. The outcomes are evaluated through the means of Human Computer Interaction.

翻译：鉴于基于深度人工智能的图像处理方法近期发生的范式转变，医学图像处理已取得显著进展。本研究提出一种名为InceptNet的新型深度神经网络，应用于医学图像处理领域，旨在通过早期疾病检测与医学图像分割提升精度与性能。同时，我们探究了用户与InceptNet应用的交互过程，构建了包含后台进程与前台用户交互的完整应用框架。快速InceptNet以经典Unet架构为基础，融合Inception模块的强大功能，在追求近似最优局部稀疏结构的同时，实现高速与低成本。通过引入具有多种并行卷积核尺寸的Inception模块，可提升网络捕捉不同尺度感兴趣区域中变化特征的能力。实验阶段，该模型在视网膜血管分割、肺结节分割、皮肤病变分割及乳腺癌细胞检测四组基准数据集上进行了测试，其中对小尺度结构图像的改进尤为显著。在所述数据集上，所提方法将准确率分别从0.9531、0.8900、0.9872、0.9881提升至0.9555、0.9510、0.9945、0.9945，表明该方法优于先前研究成果。此外，通过端到端流程探究，使用InceptNet试用版完整应用的参与者被提供了十三道多项选择题以评估所提方法，最终结果通过人机交互手段进行了评价。