On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks

Abstract

Convolutional neural network (CNN) has shown dissuasive accomplishment on different areas especially Object Detection, Segmentation, Reconstruction (2D and 3D), Information Retrieval, Medical Image Registration, Multi-lingual translation, Local language Processing, Anomaly Detection on video and Speech Recognition. CNN is a special type of Neural Network, which has compelling and effective learning ability to learn features at several steps during augmentation of the data. Recently, different interesting and inspiring ideas of Deep Learning (DL) such as different activation functions, hyperparameter optimization, regularization, momentum and loss functions has improved the performance, operation and execution of CNN Different internal architecture innovation of CNN and different representational style of CNN has significantly improved the performance. This survey focuses on internal taxonomy of deep learning, different models of vonvolutional neural network, especially depth and width of models and in addition CNN components, applications and current challenges of deep learning.

Fig. 1

Medical imaging modalities classification

Fig. 2

LeNet is a classic convolutional neural network employing the use of convolutions, pooling and fully connected layers. It was used for the handwritten digit recognition task with the MNIST dataset(dataset of 60,000 small square 28 × 28 pixel gray-scale images of handwritten single digits between 0 and 9) [87]

Fig. 3

AlexNet is a CNN architecture and its basic building blocks are max pooling and dense layer. It has imageNet classification with the deep learning techniques. This architecture will be use in pre trained model for the object detection in computer vision [89]

Fig. 4

ZfNet is a classic convolutional neural network. The design was motivated by visualizing intermediate feature layers and the operation of the classifier [90]

Fig. 5

The convolution neural network receives a fixed-size RGB image with a dimension of 224 by 224. The primary preprocessing it performs is removing out of each pixel the average RGB numbers calculated from the training sample. The image is then sent via a series of convolutional (Conv.) layers that contain filters with a tiny 3 × 3 input patch, the smallest number necessary to retain the concepts of up/down, left/right and center portion [91]

Fig. 6

GoogLeNet is a deep CNN and it has a 22-layer architecture and researchers at Googles motivated by visualizing intermediate feature layers and the operation of the classifier [92]

Fig. 7

The really quite initial point we can see in the accompanying graphic is that there is a direct link that bypasses various model levels. The core of leftover blocks is a link known as a “skip connection.” This skip connection causes the outcome to differ. Input ’X is calculated by the layers weight in the absence of the skip connection, and then a bias factor is added [94]

Fig. 8

Residual Block [94]

Fig. 9

It enables direct communication between neurons in various levels. Data transmission is regulated by a gating technique. Data can go through various levels of neural networks thanks to gating processes [95]

Fig. 10

ResNet and DenseNet are relatively similar, however there are several key distinctions. While DenseNet combine the outcome of the prior level to the next level, it employs the addition technique(+) that combines the previous layer with the subsequent layer [96]

Fig. 11

It has same accuracy as ResNet but number of layers is reduced and training time is shorter. WRN-22-8 outperform ResNet-1001 by 0.92% on CIFAR-10 and 3.46% on CIFAR-100. WRN-40-4 is 8 times faster than ResNet-1001 [97]

Fig. 12

Pyramidal Net: [98] Two popular approaches top down and bottom-up are used in pyramidal network of CNN

Fig. 13

Inception V1 [99]

Fig. 14

Inception V2 [100]

Fig. 15

Inception V3 [101]

Fig. 16

Xception is a convolutional neural network architecture that relies solely on depth wise separable convolution layers [102]

Fig. 17

A straightforward, extremely modular design network framework called ResNeXt is used to classify images. Recurring a building block that combines a group of transformations with the identical structure forms the foundation of our network [103]

Fig. 18

Fire module is comprised of squeeze convolutional layer feeding into an expand layer and that has a mix of 1 × 1 and 3 × 3 convolution filters [104]

Fig. 19

Squeeze Net is smaller network architecture that was designed as a more compact replacement for AlexNet and it perform 3 times faster and it is used in mage classification [104]

Fig. 20

UNet is a CNN network architecture that expanded with few changes in CNN architecture. This model was invented to deal with biomedical field I this image can take input and this model target was not only classifying the image it can also identify the infection as well as infection location or area [105]

Fig. 21

It is convolutional neural network architecture that can handle biomedical field and help out to perform the segmentation so it called volumetric network because detailed medical terms are not easy so experts required for detail which is high cost and automatic segmentation can help to reduce the cost in other words we can say that to achieve the high accuracy [106]

Fig. 22

It is deep encoder and decoder architecture for multi class and in this we can segmentation pixel wise [107]

Fig. 23

Unet is a CNN architecture that expanded with few changes in the CNN. This architecture was invented to segment the bio medical images like not only classify the image it can also identify the infection as well as infection area [108]

Fig. 24

Management of health care data cycle

Fig. 25

Flowchart showing the data inputs from different modalities at the beginning and a training pretrained or custom deep learning model at the conclusion