Image processing and machine learning techniques used in computer-aided detection system for mammogram screening - a review

This paper aims to review the previously developed Computer-aided detection (CAD) systems for mammogram screening because increasing death rate in women due to breast cancer is a global medical issue and it can be controlled only by early detection with regular screening. Till now mammography is the widely used breast imaging modality. CAD systems have been adopted by the radiologists to increase the accuracy of the breast cancer diagnosis by avoiding human errors and experience related issues. This study reveals that in spite of the higher accuracy obtained by the earlier proposed CAD systems for breast cancer diagnosis, they are not fully automated. Moreover, the false-positive mammogram screening cases are high in number and over-diagnosis of breast cancer exposes a patient towards harmful overtreatment for which a huge amount of money is being wasted. In addition, it is also reported that the mammogram screening result with and without CAD systems does not have noticeable difference, whereas the undetected cancer cases by CAD system are increasing. Thus, future research is required to improve the performance of CAD system for mammogram screening and make it completely automated

Digimammocad: a new deep learning-based cad system for mammogram breast cancer diagnosis with mass identification

Worldwide Breast Cancer (BC) is the most severe cancer in women. There are no outward symptoms at an early stage and the survival rate decreases with the increasing stage. So, only regular screening can save a life. Mammography is the gold standard imaging modality used for regular BC screening due to its fast acquisition and cost-effectiveness. The available Computer-Aided Detection (CAD) systems based on traditional Machine Learning (ML) systems are unable to reduce the number of undetected and false-positive breast cancer cases because of their dependency on external feature extractors that provides an inferior abstraction of feature representations. Whereas Deeper Convolutional Neural Networks (DCNNs) can automatically extract features from their inputs, and hence, a remarkable change has been observed in medical image screening. A DCNN-based CAD system suffers overfitting due to the scarcity of the annotated data and the inconsistency was reported in their performance when they were validated with external datasets. So, an effective CAD system, DIGIMAMMOCAD, was developed in this study for digital mammogram screening to diagnose BC to reduce the number of false-positive and undetected cases. It was developed using a pre-trained Residual Network of 50 layers (Resnet50) and You Only Look Once (YOLO) V2 detector where only Full Field Digital Mammograms (FFDMs) were considered. The novelty of the work lies in the increased image input layer size of the Resnet50, 2 extracted feature maps for mass identification, and the use of a small dataset. The DIGIMAMMOCAD achieved the classification accuracy, sensitivity, and specificity of 98.33 %, 0.97, and 1, respectively, along with the Average Precision (AP) of 0.91 for mass identification with the INbreast dataset. It outperformed the state-of-the-art DCNN-based systems proposed by other authors. It also achieved high performance with external datasets of different image quality after minimum adaptation in the system and reduced the false-positive and undetected cases remarkably. So, the DIGIMAMMOCAD can have a significant contribution to clinical use, and can also serve the medical fraternity as well as patients in a better way

Investigation of the Performance of different Spatial Filters toward Mammogram De-noising

De-noising is one of the important aspects of image preprocessing, mainly for medical images, in order to filter out the undesired elements without affecting any fine details. In this study spatial filters namely Mean, Median, Wiener and Gaussian filters were employed and the processed images were evaluated for Mean Squared Error (MSE), Peak Signal to Noise Ratio (PSNR) and Correlation Coefficient (CC). The numerical analysis of the accomplished results reveals that Gaussian filter delivered the best outcome for matrix size of 5x5 in terms of all chosen Metrics. Nonetheless, there were not much visual differences among all the filtered images with matrix size 5x5. Although Gaussian filter provided optimal result in this work, however some rooms are left for the improvement in future works using transform domain filters

Energy efficient elliptical concave visibility graph algorithm for unmanned aerial vehicle in an obstacle-rich environment

This paper proposes a path planning algorithm for unmanned aerial vehicle (UAV) called Elliptical Concave Visibility Graph (ECoVG). The algorithm, which is based on visibility graph (VG), overcomes the limitations of VG computation time and hence, it can be applied in real-time and in obstacle-rich environments. An experimental investigation has been done to compare the performance between ECoVG and another VG based method namely Equilateral-Space Oriented VG (ESOVG) in terms of computational time and path length. The investigation was done in identical scenarios through simulation to show that the ECoVG has a better computation time than that of ESOVG for its efficient selection of a region in calculating the path. It is also found that the proposed algorithm is energy efficient and complete since it can find a path if one exists

Computationally efficient path planning algorithm for autonomous vehicle

This paper analyses an experimental path planning performance between the Iterative Equilateral Space Oriented Visibility Graph (IESOVG) and conventional Visibility Graph (VG) algorithms in terms of computation time and path length for an autonomous vehicle. IESOVG is a path planning algorithm that was proposed to overcome the limitations of VG which is slow in obstacle-rich environment. The performance assessment was done in several identical scenarios through simulation. The results showed that the proposed IESOVG algorithm was much faster in comparison to VG. In terms of path length, IESOVG was found to have almost similar performance with VG.  It was also found that IESOVG was complete as it could find a collision-free path in all scenarios