Why am I getting an almost straight line model accuracy curve?

I've plot my model accuracy curve in train and test data and I have obtained the following curve which looks rather unusual. What does this curve indicate? Is it overfitting or underfitting? Can anyone please help me, where am I going wrong? I am working on the ABIDE dataset. I have 871 samples, I used cc400 parcellation which generated 76636 features.

I have provided the code snippet below:

import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
#create model
model = Sequential()

#add model layers
model.add(Dropout(0.2))
initializer_relu = tf.keras.initializers.HeUniform()
model.add(Dense(128, activation='relu', 
   kernel_initializer=initializer_relu, 
   kernel_regularizer=tf.keras.regularizers.l1(0.0001), input_shape= 
   (76636,)))
model.add(Dropout(0.2))
model.add(Dense(64, activation='relu', 
kernel_initializer=initializer_relu, 
kernel_regularizer=tf.keras.regularizers.l1(0.0001)))
model.add(Dropout(0.2))
initializer_sigmoid = tf.keras.initializers.GlorotUniform()
model.add(Dense(1, activation='sigmoid', 
kernel_initializer=initializer_sigmoid))

#compile model using mse as a measure of model performance
model.compile(optimizer='adam', loss='binary_crossentropy', 
metrics='accuracy')

from keras.callbacks import EarlyStopping
early_stopping_monitor = EarlyStopping(patience=3)
#train model
history= model.fit(X_train, y_train, validation_data=(X_test, y_test), 
batch_size=64 , epochs=20, callbacks=[early_stopping_monitor])

import matplotlib.pyplot as plt
print(history.history.keys())
# summarize history for accuracy
plt.plot(history.history[ 'accuracy' ])
plt.plot(history.history[ 'val_accuracy' ])
plt.title( 'model accuracy' )
plt.ylabel( 'accuracy' )
plt.xlabel( 'epoch' )
plt.legend([ 'train' , 'test' ], loc= 'lower right' )
plt.show()