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SN Computer Science (2020) 1:81 https://doi.org/10.1007/s42979-020-0109-6

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ORIGINAL RESEARCH

Detection of Ovarian Cyst in Ultrasound Images Using Fine‑Tuned VGG‑16 Deep Learning Network

Sakshi Srivastava1 · Prince Kumar1 · Vaishali Chaudhry2 · Anuj Singh3

Published online: 13 March 2020 © Springer Nature Singapore Pte Ltd 2020

AbstractOvaries play a vital role in the female reproductive system as they are responsible for the production of egg or ovum required during the fertilization. The female ovaries very often get affected with cyst. An enlarged ovarian cyst can lead to torsion, infertility and even cancer. Therefore, it is very important to diagnose it as soon as possible. For the diagnosis of an ovarian cyst, ultrasound test is conducted. We collected the sample ultrasound images of ovaries of different women and detected whether ovarian cyst is present or not. The proposed work employs the traditional VGG-16 model fine-tuned with our very own dataset of ultrasound images. A VGG-16 model is a 16-layer deep learning neural network trained on ImageNet dataset. Fine-tuning is done by modifying the last four layers of VGG-16 network. Our model is able to determine whether the ultrasound images shows ovarian cyst or not. An accuracy of 92.11% is obtained. The accuracy and loss curves are also plotted for the proposed model.

Keywords Ultrasound · Ovarian cyst · Ovarian torsion · VGG-16 · Fine-tuning

Introduction

Female reproductive system consists of ovaries, which are located in lower abdomen on both sides of uterus, left and right. They are responsible for producing egg or ovum and also the estrogen and progesterone hormones. Female ova-ries can easily get affected with cyst which is a fluid-filled sac. Generally, cysts are painless. A female having ovarian cyst usually goes through an irregular menstruation cycle at early stages. The symptoms of an ovarian cyst include pain in lower back or thighs, nausea and vomiting, abdominal swelling and pelvic pain before or after menstruation.

The researchers analyzed the morphological and func-tional changes of follicles during their development [1]. Generally, ovarian cyst may go away with little or no treat-ment, but in some cases it leads to ovarian torsion. An ovar-ian torsion occurs when the large cyst causes the ovary to move from its original position. It is very painful and leads to cutoff of blood supply in the ovaries. It may lead to death or damage of the ovarian tissue.

A three-dimensional computer imaging model and corre-lation program is used for developing a new system of map-ping and monitoring follicles [2]. Therefore, it is important to diagnose an ovarian cyst as early as possible. Doctors conduct ultrasound test in order to check whether the person has ovarian cyst or not. An ultrasound or ultrasonography is an imaging test which helps in producing images of an individual’s internal organs by using high-frequency sound waves. With the help of an ultrasound test, doctors can deter-mine the size, location, shape and the composition of the cyst present in the ovary.

We have used a fine-tuned VGG-16 deep learning net-work in order to detect whether an ovarian cyst is present or not (Fig. 1).

This article is part of the topical collection “Advances in Computational Intelligence, Paradigms and Applications” guest edited by Young Lee and S. Meenakshi Sundaram.

* Sakshi Srivastava sakshigunja@gmail.com

1 Computer Science and Engineering Department, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

2 Department of Biotechnology, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

3 Technology Business Incubator, Graphic Era Deemed to be University, Dehradun, Uttarakhand, India

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Ovarian Cysts

Female ovaries can be diagnosed with many types of cysts. Major types of ovarian cysts are listed below.

Functional Cysts

Functional cysts are the most common type of ovarian cysts developed in the female body during the menstrual cycle. They can go away in 3–4 months with or without medication. Functional cysts are generally less painful compared to other types of ovarian cysts. They are gener-ally noncancerous and of two types: follicle [3] cyst and corpus luteum cyst.

During the normal menstrual cycle, the egg or the ovum is produced by the ovary once in a month and is grown inside a tiny sac called follicle. The follicle breaks during the maturation phase of the egg. If the follicle does not break to produce egg, then it results in the formation of follicle cyst. It is usually painless and can vanish in 2–3 months.

Corpus luteum is a mass of cell into which the folli-cle shrinks when the egg is released from it. It helps the ovaries to prepare for the next ovulation in the upcoming menstrual cycle. If the sac does not shrink and reseals itself after releasing the egg, then some fluid builds up inside it and leads to the formation of corpus luteum cyst. It is a painful form of ovarian cyst as it may lead to bleed-ing and twisting of the ovaries. A corpus luteum cyst can generally go away in a week time (Fig. 2).

Endometrioma or Endometrioid Cysts

A stage occurs when the tissue similar to the lining of the uterus grows outside it (in the ovaries), known as endome-triosis. Endometrioma is caused by endometriosis.

Endometrioid cysts are sometimes filled with dark red-dish brown blood and may cause problems with fertility of a woman (Fig. 3).

Dermoid Cyst

It occurs in the ovaries during the reproductive period of a woman and can lead to torsion, infertility, rupture and even ovarian cancer. Conventional surgery or laparoscopy is generally used to remove a dermoid cyst from the female body (Fig. 4).

Hemorrhagic Ovarian Cysts

Sometimes, occurrence of bleeding in the ovary leads to the formation of HOC. It results in abdominal pain in the body of the female (Fig. 5).

Polycystic Ovary Syndrome

In a female having PCOS, her ovaries may develop many small collections of follicles. These follicles stop to release the eggs. PCOS is a hormonal disorder common in woman

Fig. 1 a Normal ovary, b ovary having an ovarian cyst

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during her reproductive years. It leads to irregular men-struation (Fig. 6).

Related Work

The researchers proposed a new method for detection of follicles using active contour without edges for image seg-mentation. Five geometric features were extracted. A false acceptance rate (FAR) of 12.6% and a false rejection rate

Fig. 2 Functional ovarian cysts

Fig. 3 Endometrioid cyst

Fig. 4 Dermoid cyst

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(FRR) of 5.7% were obtained [4]. This work was further modified and updated by applying edge-based segmenta-tion on the ultrasound images of ovaries in order to detect ovarian cyst [5].

Jyothi R Tegnoor detected and classified ovarian cysts by preprocessing the ultrasound images using edge detec-tion, histogram equalization, active contours without edges method, and morphological operations were applied for image segmentation. Seven geometric features are extracted, and SVM was used to classify the image into follicle or nonfollicle with false acceptance rate (FAR) of 2.00% and false rejection rate (FRR) of 0.32% [6].

Devesh D. Nawgaje et al. proposed a hardware imple-mentation of the genetic algorithm (GA) for image seg-mentation by selecting an optimal threshold value. The steps involved in GA included coding scheme (eight-bit binary code), initial group, fitness function, selection, crossover, mutation, reinsertion and termination condition (when genetic algebra = 50). A Digital Signal Processor TMS320C6713 was used for implementation purpose as it was time-efficient [7].

An automated algorithm was proposed by Sandy R. et al. for detecting ovarian cysts from the ultrasonogram images. The ultrasonogram images were first preprocessed by apply-ing different morphological operations in order to remove noise and enhance contrast. Then, scanline thresholding was applied both vertically and horizontally and the connected components were detected and labeled on which geometric feature extraction was done. The results were validated after analyzing the ROC curve thoroughly. SVM was used for classification. An accuracy of 90% was obtained [8].

The researchers employed a fuzzy logic method for detec-tion of follicles in ultrasound images. Firstly, contourlet transform was done for despeckling the ultrasound images of ovaries and segmentation was done using active contours without edge method. Classification was done by using fuzzy logic. Seven geometric features were used as inputs to the fuzzy logic block of the fuzzy inference system, which clas-sified the data into a follicle class or a nonfollicle class [9].

Dency Treesa John et al. used ANN to classify ovarian cysts. The preprocessed images of ovarian cysts were fed to the ANN as an input, and the model was then trained. The

Fig. 5 Hemorrhagic ovarian cyst (HOC)

Fig. 6 Polycystic ovary syndrome (PCOS)

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ANN was then able to classify the images into dermoid and follicular [10].

Continuous wavelet transform (CWT) is applied for the segmentation of 3D ultrasound images of ovary by determin-ing the centers of follicles, their shape and the position of border [11].

G. Vasavi et al. proposed a study to detect and classify ovarian cysts from their ultrasound images. The follicles in the ultrasound image were detected by applying Gaussian

low-pass filter for preprocessing, canny operator for edge detection and 3σ-intervals for classification around the mean. Additionally, morphological dilation was used for eliminating the noisy edges in the preprocessed image. Later, watershed segmentation and optimal thresholding method was applied using sobel as the operator in order to detect the follicles [12].

Dataset Formation

An appropriate dataset is vital for the proper functioning of any deep learning framework. We have formed the data-set for pretraining of the model by collecting 240 ultra-sound images for normal ovaries and those having ovarian cyst. The dataset is split into two parts: training set and validation test. A total of 160 images are present in the training set and 80 images in the validation set (Table 1).

Table 1 Number of images present in the dataset used for ovarian cyst detection

Dataset Category Image count

Training set Normal 80Ovarian cyst 80

Total 160Validation set Normal 40

Ovarian cyst 40Total 80Entire dataset 240

Fig. 7 Architecture of a VGG-16 model

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Methodology

A VGG-16 model is used as the pretrained deep learn-ing model [13]. The VGG in VGG-16 stands for “Visual Geometry Group.” Visual Geometry Group from the Uni-versity of Oxford came up with the idea of creating this particular 16-layer network and trained it on ImageNet dataset. A traditional VGG-16 model consists of multiple 3 × 3 kernel-sized filters which help the model in learn-ing more complex features by increasing the depth of the network. The convolutional layers in VGG are followed by three fully connected layers (Fig. 7).

To increase the accuracy of a traditional VGG-16 model, we have performed fine-tuning using our own data-set of ultrasound images.

Fine‑Tuning

In fine-tuning, an already existing pretrained model is modified with the similar type of task which it earlier performed. This helps us in taking benefit of the feature extraction that happens in the front layers of the network. Fine-tuning in a network saves time as model is not to be developed from scratch [14]. In our study, we have used VGG-16 model and modified its last four layers by training it on the ovarian cyst dataset using convolutional neural network. This is done in order to achieve high accuracy.

We have fixed all the layers of the VGG-16 model except for the last four layers and displayed the summary of entire employed model as shown in Fig. 8.

Basic Steps Involved in the Algorithm

Step 1 Load the traditional VGG-16 model.Step 2 Freeze all the layers in the model except the last 4.Step 3 Check the trainable status of the individual layers.Step 4 Create the model.Step 5 Add the VGG convolutional base model.Step 6 Add new layers (flatten, dense and dropout) to it.Step 7 Set dropout value as 0.2.Step 8 Check the number of trainable parameters by going

through the summary of the model.Step 9 Create the data generator for the training data.Step 10 Create the data generator for the validation or

test data.Step 11 Compile and train the model.

Results and Discussion

We have trained the last four layers of the VGG-16 tradi-tional model with the dataset of ultrasound images of ovaries of different women in order to increase the accuracy of the model. We obtain an accuracy of 92.11% (Figs. 9, 10).

The curve describing the performance as a function of the sample size of the training data is often called the learning curve [15]. This learning curve is used for plotting accuracy and loss curves.

For the further evaluation of the fine-tuned VGG-16 model developed, an accuracy curve and a loss curve are plotted. The accuracy curve shows the rate of change of accuracy for both training and validation sets, and the loss curve shows the rate of change of accuracy for both training and validation sets (Figs. 11, 12).

Conclusion

Since ovaries are responsible for the production of eggs in female body, it is important to have a healthy ovary in the female reproductive system. Cyst formation often occurs once in a lifetime in the ovaries of every female in this world. If the size of the ovarian cyst becomes very large, then it may cause torsion, infertility, pelvic pain and even cancer. Therefore, it is important to detect them on time.

The proposed algorithm in this paper is able to detect whether a female is having ovarian cyst or not. The study uses the traditional VGG-16 deep neural networking model, which is a 16-layer neural network model trained on Ima-geNet dataset. We have used this VGG-16 model as it is except for the last four layers, which are modified slightly by training them on our own dataset comprised of collection of ultrasound images of ovaries of different females. This process of modifying the existing pretrained model (in our Fig. 8 Summary of the model employed

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case, VGG-16 deep learning model) is called fine-tuning. Fine-tuning is done in order to achieve higher accuracy as compared to that if done without it.

Our algorithm is able to achieve an accuracy of 92.11%, which is good enough as compared to the previous work done on the detection of ovarian cyst in the ultrasound images.

Future Work and Applications

Our algorithm of fine-tuned VGG-16 deep neural network-ing model can further be used to classify the ovarian cysts into their types including functional, dermoid, hemorrhagic

ovarian cyst (HOC) and polycystic ovary syndrome (PCOS). It can also be used for the detection of ovarian cancer easily so that the person suffering from it can undergo the treat-ment as soon as possible.

The algorithm can act as a boon in the medical life as it can be used for detecting other similar types of diseases from the ultrasound images.

References

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Fig. 9 Proposed methodology

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Fig. 10 Training phase of the fine-tuned VGG-16 model

Fig. 11 Accuracy curve

Fig. 12 Loss curve