  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.
