FDP-Net: Fourier transform guided lightweight depthwise and pointwise dynamic pooling based neural network for medical image classification

Nowadays, deep learning-based medical image classification has become essential, especially in developing countries because of the high volume of patients with less medical professionals as well as required infrastructures. Deep learning models often help in the early detection of diseases; however, it require a high amount of processing power, and sometimes it becomes less scalable for various computer-aided diagnosis. To this end, in this paper, a lightweight Fourier Transform guided Depth and Pointwise Dynamic Pooling based Neural Network (FDP-Net), has been proposed for medical image classification. This paper introduces a Depth and Pointwise Feature Fusion (DPFF) block for learning the important features with less computation and without increasing the model parameters. It also proposes a dynamic pooling technique, an alternative to traditional max-pooling, which dynamically selects the important features. The proposed FDP-Net model is trained to classify medical images with the guidance of Fourier Transformation and multitask loss function, which makes the model converge faster and reduces overfitting. The proposed model has been tested on Acute Lymphoblastic Leukemia (ALL) dataset, Peripheral Blood Cell (PBC) dataset, and Raabin White blood Cell (Raabin-WBC) dataset, and it outperforms the state-of-the-art models with 100%, 98.13% and 96.79% classification accuracies, respectively. Additionally, the proposed model is made with only 0.349 million parameters, thereby enabling faster processing. Code will be avilabe at https://github.com/asfakali/FDP-Net.