MicrosMobiNet: A Deep Lightweight Network With Hierarchical Feature Fusion Scheme for Microscopy Image Analysis in Mobile-Edge Computing

In recent advancements of lightweight deep architectures for edge devices, most of the works follow a typical MobileNet pipeline designed for computer vision tasks which is not very appropriate for microscopy image analysis. Certainly, the design of the dedicated lightweight network for highly complex microscopy image analysis has not been attempted so far. Therefore, this work proposes a new deep lightweight network, “MicrosMobiNet” having multiscale feature extraction mechanism for bright-field microscopy image analysis on a mobile-edge computing framework. It consists of three key attributes—depth-wise separable convolution for making the network lightweight, multiple kernels with hierarchical feature fusion to extract complex features, and residual connection to keep network deep. Experimental validations have been conducted by two different microscopy image data sets—plant (potato tuber) and histopathology (cancer cell) generated by two different image generation modalities. In the experiment, multiclass and multilabel classification tasks have been evaluated by measuring accuracy, F1-score, and error. In the ablation study, the key attributes of the network have been verified. The results and analysis show that the MicrosMobiNet can achieve classification accuracy up to 98.43% and 96.25% for plant and cancer cells with minimum error 8.38% and 10.03%, respectively. In a comparative study, the MicrosMobiNet outperforms the existing lightweight state-of-the-art methods with fewer parameters (1.9M) and FLOPs count (42M). Finally, the new network has been implemented on an edge device, Smartphone (Android platform) which is working satisfactorily with high speed (140 ms) and very low memory (7.4 MB). Hence, the network exhibits its superiority in bright-field microscopy image analysis on mobile-edge computing platforms in a lightweight deep learning framework.